dc.relation | Abdelmaguid, T. (2015). A neighborhood search function for flexible job shop scheduling with separable sequence-dependent setup times. Applied Mathematics and Computation, 260, 188–203. Recuperado de http://doi.org/10.1016/j.amc.2015.03.059.
Adam,S.,& Bruno, R. (2015). ReviewPareto’s 80/20 law and social differentiation: A social entropy perspective. Public Relations Review, 41, 178–186. Recuperado de http://dx.doi.org/10.1016/j.pubrev.2014.11.006.
Ahmad,S., Wong,K., Tseng,M., & Wong,W. (2018). Sustainable product design and development: A review of tools, applications and research prospects. Resources, Conservation & Recycling, 132, 49–61.
Ahmadi, E., Zandieh, M., Farrokh, M., & Emami, S. (2016). A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms. Computers and Operations Research, 73, 56-66. doi:10.1016/j.cor.2016.03.009.
Ahmed,W., Ashraf,M., Khan,S., Sarpong,S., Arhin,F., Sarpong., & Najmi,A. (2020). Analyzing the impact of environmental collaboration among supply chain stakeholders on a firm’s sustainable performance. Operations Management Research , 13, 4–21.
Akbar,M., & Irohara,T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production 205 (2018) 866e883, 205, 866- 883. Recuperado de https://doi.org/10.1016/j.jclepro.2018.09.100.
Adewuyi, O.B., Shigenobu, R., Senjyu, T., Lotfy, M.E., & Howlader, A.M. (2019). Multiobjective mix generation planning considering utility-scale solar PV system and voltage stability: Nigerian case study. Electr. Power Syst. Res. 168, 269–282. https://doi.org/10.1016/j.epsr.2018.12.010
Akgun, I.,& Erdal,H. (2019). Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS. Computers & Industrial Engineering, 129, 512-528. Obtenido de https://doi.org/10.1016/j.cie.2019.02.004.
Allahverdi, A. (2015). The third comprehensive survey on scheduling problems with setup times/costs. . European Journal of Operational Research, 246(2), 345-378.
Allahverdi, A., Ng, C. T., Cheng, T. E., & Kovalyov, M. Y. (2008). A survey of scheduling problems with setup times or costs. European Journal of Operational Research, 187(3), 985-1032.
Almahmoud,E., & Doloi, H. (2015). Assessment of social sustainability in construction projects using social network analysis. Facilities, 33, 152-176. Recuperado de https://doi.org/10.1108/F-05-2013-0042.
Azzouz, A., Chaabani, A., Ennigrou, M., & Said, L. (2020). Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization. Applied Artificial Intelligence, 34:6, 433-455, doi: 10.1080/08839514.2020.1723871.
Amin,N., Noah,R.,Quek, J., Oxley ,J., & Rusli,B. (2020). Perceived physical demands in relation to work-related musculoskeletal disorders among nurses. Materials Today: Proceedings , 7(21),1-6. Recuperado de https://doi.org/10.1016/j.matpr.2020.01.196.
Amiri, F., Shirazi, B., & Tajdin, A. (2019). Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop. Applied Soft Computing Journal 75 (2019) 190–202, 75, 190–202. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.015.
Amini, M., & Bienstock, C. (2014). Corporate sustainability: an integrative definition and framework to evaluate corporate practice and guide academic research. Journal of Cleaner Production, 76, 12–19. Recuperado de http://doi.org/10.1016/j.jclepro.2014.02.016.
An, Y., Chen, X., Zhang, J., & Li,L. (2020). A hybrid multi-objective evolutionary algorithm to integrate optimization of the production scheduling and imperfect cutting tool maintenance considering total energy consumption. Journal of Cleaner Production, 1-28. Recuperado de https://doi.org/10.1016/j.jclepro.2020.121540.
Ardjmand,E., Sanei,O., & Youssef,O. (2019). Using list-based simulated annealing and genetic algorithm for order batching and picker routing in put wall based picking systems. Applied Soft Computing Journal , 75, 106–119. Recuperado de https://doi.org/10.1016/j.asoc.2018.11.019.
Ari,E., Karatepe,O., Rezapouraghdam,H., & Avci,V. (2020). A Conceptual Model for Green Human Resource Management: Indicators, Diferential Pathways,and Multiple Pro-Environmental Outcomes. Sustainability doi:10.3390/su12177089 www.mdpi, 12, 1-18. doi:10.3390/su12177089 www.mdpi.
Arcidiacono, G., Costantino, N., Yang, K., 2016. The AMSE lean six sigma governance model. Int. J. Lean Six Sigma 7, 233e266. https://doi.org/10.1108/IJLSS-06-2015- 0026
Armindo,J., Fonseca,J., Abreu,I & Toldy,T. (2019). Perceived importance of sustainability dimensions in the Portuguese metal industry. International Journal of Sustainable Development & World Ecology, 26:2, 154-165. doi: 10.1080/13504509.2018.1508524.
Aryanny,E.,& Iriani. (2019). Analysis of Quality Management by Implementing Total Quality Management Based on Deming Prize. International Conference on Science and Technology , 2-7. doi:10.1088/1742-6596/1569/3/032015.
Aschauer,A., Roetzer.,A., Steinboeck,A., & Kugi. (2017). An Efficient Algorithm for Scheduling a Flexible Job Shop with Blocking and No-Wait Constraints. IFAC PapersOnLine, 50 (1), 12490–12495.
Autuori,J., Hnaien,F.,& Yalaoui. (2016). Three metaheuristics improved by a mapping method. IFAC (International Federation of Automatic Control), 49-12, 1472–1477. doi: 10.1016/j.ifacol.2016.07.779.
Bahar, N., Noor, Z., Aris, A., & Kamaruzaman, N. (2020). An indicator framework approach on manufacturing water assessment towards sustainable water demand management. Journal of Environmental Treatment Techniques, 8(3), 875-883.
Balaraju, G., Venkatesh, S., & Reddy, B. (2014). Multi-objective flexible job shop scheduling using hybrid differential evolution algorithm. International Journal of internet Manufacturing and Services, 3(3), 226-243.
Bamgbade,J., Kamaruddeen,M., & Nawi,M. (2017). Malaysian construction firms' social sustainability via organizational innovativeness and government support: The mediating role of market culture. Journal of Cleaner Production, 154, 114-124. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.03.187.
Banco Mundial. (2016). Datos sobre las cuentas nacionales del Banco Mundial. Obtenido de http://datos.bancomundial.org/indicador/NY.GDP.PCAP.CD
Banerjee,S.,& . (2019). The occupational illness of slum dwellers across industries: A case study in West Bengal. International Journal of Industrial Ergonomics , 73 (10), 1-9. Recuperado de https://doi.org/10.1016/j.ergon.2019.102834.
Belhadi,A.,Sha’ri,Y., Touriki,F & El Fezazi,S. (2018). Lean production in SMEs: literature review and reflection on future challenges. Journal of Industrial and Production Engineering, 35 (6), 368–382.
Bihari M., & Kane P. (2021). Evaluation and Improvement of Makespan Time of Flexible Job Shop Problem Using Various Dispatching Rules—A Case Study. In: Kalamkar V., Monkova K. (eds). Mechanical Engineering, 11 (9), 21-31. Recuperado de https://doi-org.ezproxy.unal.edu.co/10.1007/978-981-15-3639-7_73.
Bi,X.,& Wang,C. (2018). A niche-elimination operation based NSGA-III algorithm for many-objective optimization. Appl Intell, 48,118–141. doi:10.1007/s10489-017-0958-4.
Bissoli, D.,Altoe,W., Mauri,G & Amaral,A. (2018). A simulated annealing metaheuristic for the bi-objective flexible job shop scheduling problem,. International Conference on Research in Intelligent and Computing in Engineering (RICE), San Salvador, 1-6, doi: 10.1109/RICE.2018.8627907.
Bissoli,D., Zufferey,N., & Amaral,A. (2019). Lexicographic optimization-based clustering search metaheuristic for the multiobjective flexible job shop scheduling problem. International Transactions in Operational Research, 1–26.
Brandimarte, P. (1993). Routing and scheduling in a flexible job shop by tabu search. Annals of Operations Research, 41 (3), 157-183. Recuperado dehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-34250084648&doi=10.1007%2fBF02023073&partnerID=40&md5=e855d121de089837fab8733d864abc60.
Brit, J. (2015). Stochastic Goal Programming and a Metaheuristic for Scheduling of Operating Rooms. Canadá: Universidad de Windsor .
Bukchin, Y., Hanany, E., 2020. Decentralization cost in two-machine job-shop scheduling with minimum flow-time objective. IISE Trans. 52(12), 1386–1402. https://doi.org/10.1080/24725854.2020.1730528
Burki,u.,Ersoy,p., & Dahlstrom,B. (2018). Achieving triple bottom line performance in manufacturer-customer supply chains: Evidence from an emerging economy. Journal of Cleaner Production , 197, 1307-1316. Recuperado de https://doi.org/10.1016/j.jclepro.2018.06.236.
Büyüközkan, B., Kayakutlu,G.,& Karakadılar,I. (2015). Assessment of lean manufacturing effect on business performance using Assessment of lean manufacturing effect on business performance using. Expert Systems with Applications, 42, 6539–6551. Recuperado de http://dx.doi.org/10.1016/j.eswa.2015.04.016.
Cai,Y., Wang,F., Tan,G., Hu,Z., Wang,Y., Lun,W., Wu,W., Liu,Y & Zhou,R. (2019). Hybridization of Bornean Melastoma: implications for conservation of endemic plants in Southeast Asia. Botany Letters, 166 (2), 117–124. Recuperado de https://doi.org/10.1080/23818107.2019.1585284.
Caldeira R.H., & Gnanavelbabu A. (2021). Solving the Flexible Job Shop Scheduling Problem Using a Hybrid Artificial Bee Colony Algorithm. En S. N. Vijayan S., Trends in Manufacturing and Engineering Management. Springer.
Caldwell,J., Caldwell,L., Thompson,L., & Lieberman,H. (2019). Fatigue and its management in the workplace. Neuroscience and Biobehavioral Reviews, 96, 272–289.
Calleja, G., & Pastor, R. (2013). A dispatching algorithm for flexible job-shop scheduling with transfer batches: an industrial application. Production Planning & Control. Obtenido de http://www.tandfonline.com/doi/abs/10.1080/09537287.2013.782846
Canales,A., & Campos,A. (2016). Modelamiento Predictivo de la Pérdida Auditiva Laboral, Relacionada con el Tratamiento de Absorción Acústica en una Industria Metal-Mecánica en Chile. Ciencia & trabajo, 11(9), 21-29.
Carpio de los Pinos,A., & González,M . (2020). Development of the Protocol of the Occupational Risk Assessment Method for Construction Works: Level of Preventive Action. International Journal of Environmental Research and Public Health, 17, 6369, 1-33. doi:10.3390/ijerph17176369.
Castro, V., Herrera, R., & Villalobos, M. (2020). Development of a web software to generate management plans of software risks. Revista Información Tecnológica, 31(3), 135-148. Recuperado de http://dx.doi.org/10.4067/S0718-07642020000300135.
CCMPC. (2019). Cámara de Comercio de Manizales por Caldas. Informe económico anual - Manizales y Caldas. Manizales.
CCMPC. (2014). Camara de Comercio de Manizales por Caldas.Caracterización sector metalmecánico de la ciudad de Manizales. Manizales.
CCS. (2019). Consejo Colombiano de Seguridad. Cómo le fue a Colombia en accidentalidad, enfermedad y muerte laboral en 2018. Obtenido de https://ccs.org.co/como-le-fue-a-colombia-en-accidentalidad-enfermedad-y-muerte-laboral-en-2018/
CESEDEN. (2018). Centro Superior de la Defensa Nacional. La Inteligencia artificial aplicada a la defensa.
Charnes,A., Cooper,W.,& Ferguson,R. (1995). Optimal Estimation of Executive Compensation by Linear Programming. Management Science, 138-151, Recuperado de https://doi.org/10.1287/mnsc.1.2.138.
Charniak,E & McDermott. (1985). Introduction to Artificial Intelligence. Reading, Mass. Addison-Wesley.
Chen, H., Jiang, Z., Zuo, L., & Zhang, Y. (2015). Multi-objective flexible job-shop scheduling problem based on NSGA-Ⅱ with close relative variation. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 46(4), 344–350. Recuperado de http://doi.org/10.6041/j.issn.1000-1298.2015.04.051.
Chen, Y., Wang, J., & Yang, Z. (2015a). The human factors/ergonomics studies for respirators: a review and future work. International Journal of Clothing Science and Technology, 27(5), 652–676. Recuperado de http://doi.org/10.1108/IJCST-06-2014-0077.
Chen,G., Johnson,K., Morales,E., Ibáñez,A., & Lin,T. (2018). A high-Oil castor cultivar developed through recurrent selection. Industrial Crops & Products, 111, 8–10. Recuperado de http://dx.doi.org/10.1016/j.indcrop.2017.09.064.
Chen, X., Liang, Y., Sterna, M., Wang, W., & Błażewicz, J. (2020a). Fully polynomial time approximation scheme to maximize early work on parallel machines with common due date. Eur. J. Oper. Res. 284, 67–74. https://doi.org/10.1016/j.ejor.2019.12.003
Chen,X., An,Y., Zhang,Z., & Li,Y. (2020). An approximate nondominated sorting genetic algorithm to integrate optimization of production scheduling and accurate maintenance based on reliability intervals. Journal of Manufacturing Systems , 54, 227–241.
Cheng,C., , Zhijun,C., & Chenghua,S. (2003). A novel design method: a genetic algorithm applied to an erbium-doped fiber amplifier. Optics Communications, 227 (11), 371–382.doi:10.1016/j.optcom.2003.09.055.
Chetan., Ghosh, S., & Venkateswara Rao, P. (2015). Application of sustainable techniques in metal cutting for enhanced machinability: a review. Journal of Cleaner Production, 100, 17–34. Recuperado de http://doi.org/10.1016/j.jclepro.2015.03.039.
Chiang, T., & Lin, H. (2011). Flexible job shop scheduling using a multiobjective memetic algorithm. . Proceedings of the 7th International Conference on Intelligent Computing (ICIC). Zhengzhou, China, 49-56. Recuperado de http://link.springer.com/chapter/10.1007.
Chiang, T., & Lin, H. (2013). A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling. International Journal of Production Economics, 141(1), 87–98. Recuperado de http://doi.org/10.1016/j.ijpe.2012.03.034.
Chunguang,B., Sarpong,S.,& Sarkis,J. (2017). An implementation path for green information technology systems in the Ghanaian mining industry. Journal of Cleaner Production, 164, 1105-1123.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2015a). Design of a Novel Methodology for Multiobjective Anlysis in a Job Shop Manufacturing System, Based on Comparison of Subpopulations. In Proceedings of the 27th European Conference on Operational Research. July 12-15, 2015. Glasgow, Scotland: University of Strathclyde, 224. Recuperado de https://www.euro- online.org/media_site/reports/EURO27_AB.pdf.
Coca, G., Castrillón, O., & Ruiz, S. (2015b). El sonido y la iluminación en la programación multiobjetivo de un sistema de manufactura tipo “Job Shop”. Memorias del décimo primer congreso de Investigación Operativa – ÓPTIMA 2015. Octubre 18 al 21 de 2015. Ediciones Universitarias : Antofagasta - Chile.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2016). Programación de un Sistema de Fabricación tipo “job shop” bajo un Enfoque de Sostenibilidad. Revista Información Tecnológica, 27(6), 31-52. doi: 10.4067/S0718-07642016000600005.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2018). Los grupos de interés en la programación de producción de sistemas de manufactura “Job Shop” utilizando un algoritmo genético multiobjetivo. Proceeding of the XIX Latin Iberoamerican Conference on Operations Research - CLAIO 2018. September 24-27, 2018. Lima, Perú: @ Sociedad Peruana de Investigación Operativa (SOPIOS), 55, Recuperado de http://www.sopios.org.pe/static/claio/proceeding.pdf.
Coca, G. A., Castrillón, O. D., & Ruiz, S. (2019a). Los grupos de interés en la programación de producción de un sistema de manufactura “Job Shop” . Revista EIA, 16(32), 65-84. Recuperado de https://doi.org/10.24050/reia.v16i32.1236.
Coca, G. A., Castrillón, O. D., Ruiz, S., Mateo-Sanz, J., & Jiménez, L. (2019b). Sustainable evaluation of environmental and occupational risks scheduling flexible job shop manufacturing systems. Journal of Cleaner Production, 209, 146-168. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.193.
Coello, A., Christiansen & Aguirre,H. (1995). Multiobjective design optimization of counterweight balancing of a robot arm using genetic algorithms. Proceedings of 7th IEEE International Conference on Tools with Artificial Intelligence. Herndon, VA, USA , 20-23.doi: 10.1109/TAI.1995.479374.
Comisión Europea. (2016). Acuerdo de París. Obtenido de https://ec.europa.eu/clima/policies/international/negotiations/paris_es
Confecámaras. (2017). Contribución de las iniciativas clúster al desarrollo regional. Bogotá.
Conpes 3918. (2018). Estrategia para la implementación de los objetivos de desarrollo sostenible (ods) en Colombia. Bogotá: Consejo Nacional de Política Económica Y Social.Departamento Nacional De Planeación.
Cooper,M. (2019). The efficacy of industrial safety science constructs for addressing serious injuries & fatalities (SIFs). Safety Science , 120, 164–178. Recuperado de https://doi.org/10.1016/j.ssci.2019.06.038.
Corentin, L. (2019). Integrating waste minimization concerns in operations scheduling. These de Doctorat de L’Universite de Lyon (Ecole Doctorale Informatique et Mathématiques de Lyon). Recuperado de : http://theses.insa-lyon.fr/publication/2019LYSEI111/these.pdf.
Cuayal,J.,& Romero,L. (2016). Documentación del sistema de gestión ambiental en el marco de la NTC-ISO 14001 para la E.S.E hospital universitario San Jorge de la ciudad de Pereira. Pereira, Risaralda: Universidad Tecnológica de Pereira.
Cui,Z., Chang,Y., Zhang,J., Cai,X., & Zhang,W. (2019). Improved NSGA-III with selection-and-elimination operator. Swarm and Evolutionary Computation , 49, 23–33. Recuperado de https://doi.org/10.1016/j.swevo.2019.05.011.
D´Atlas. (2018). Manizales Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/49?locale=es-col
D´Atlas. (2018). Medellín Met. Obtenido de http://datlascolombia.bancoldex.com/#/location/34
Dai,M., Tang,D., Giret,A., Salido.M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59, 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.
DANE. (2020). Departamento Administrativo Nacional de Estadística. Encuesta mensual manufacturera con enfoque territorial (EMMET) Históricos. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/industria/encuesta-mensual-manufacturera-con-enfoque-territorial-emmet/emmet-historicos
DANE. (2020). Departamento Administrativo Nacional de Estadística. Producto Interno Bruto (PIB) Base 2015. Obtenido de https://www.dane.gov.co/index.php/estadisticas-por-tema/cuentas-nacionales/cuentas-nacionales-trimestrales
Darmawan,M., Widhiarti,R., & Teniwut, Y. (2018). Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. Journal of Cleaner Production , 191, 273- 282. Recuperado de https://doi.org/10.1016/j.jclepro.2018.04.228.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms . London: Wiley & Sons.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions on, 6(2), 182-197.
Deb, K., & Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601. Recuperado de http://doi.org/10.1109/TEVC.2013.2281535.
Deliktas, D., Torkul,O., & Ustun,O. (2019). A flexible job shop cell scheduling with sequence-dependent family setup times and intercellular transportation times using conic scalarization method. International Transactions in Operational Research, 26, 2410–2431.
Demir, Y., & İşleyen, S. (2014). An effective genetic algorithm for flexible job-shop scheduling with overlapping in operations. International Journal of Production Research, 52(13), 3905-3921.
Dendena, B., & Corsi, S. (2015). The Environmental and Social Impact Assessment (ESIA): a further step towards an integrated assessment process. Journal of Cleaner Production, 108, 965–977. Recuperado de http://doi.org/10.1016/j.jclepro.2015.07.110.
Deng,Q., Gong,G., Gong,X., Zhang,L.,Liu,X., & Ren,Q. (2017). A Bee Evolutionary Guiding Nondominated Sorting Genetic Algorithm II for Multiobjective Flexible Job-Shop Scheduling. Computational Intelligence and Neuroscience, 1-21. Recuperado de https://doi.org/10.1155/2017/5232518.
D'Eusanio,M ., Zamagni,A., & Petti,L. (2019). Social sustainability and supply chain management: Methods and. Journal of Cleaner Production , 235, 178- 189. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.323.
Dietz,J., & Mulder, H. (2020). A History of Organisation and ICT. En Enterprise Ontology. The Enterprise Engineering Series. Springer, Cham.
Digalwar,A., Dambhare,S.,& Saraswat,S. (2020). Social sustainability assessment framework for indian manufacturing industry. Materials Today: Proceedings, 8(11), 1-8- Recuperado de https://doi.org/10.1016/j.matpr.2019.12.226.
DNP. (2009). Departamento Nacional de Planeación. Metalmecánica. Obtenido de https://colaboracion.dnp.gov.co/CDT/Desarrollo%20Empresarial/metalmecanica.pd
DNP. (2018). Departamento Nacional de Planeación. Política para el mejoramiento de la calidad del aire. Bogotá.
DNP. (2017). Departamento Nacional de Planeación. Diagnóstico de Crecimiento Verde. Análisis macroeconómico y evaluación del potencial de crecimiento verde. Bogotá.
Domínguez Machuca, J. A., Álvarez Gil, M. J., Domínguez Machuca, M. A., González, S. G., & Ruiz Jiménez, A. (1995). Dirección de operaciones-Aspectos estratégicos en la producción y los servicios. McGraw Hill, España.
Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 26(1), 29-41.
Dóro, L., Pandolfo.,A., & Panosso,R . (2020). Developing a specific structured procedure to assess sustainability performance in manufacturing processes. Journal of Cleaner Production, 7(11), 1- 40. Recuperado de https://doi.org/10.1016/j.jclepro.2020.122404.
Drechsel,D., Barlow,C., Bare,J., Jacobs,N.,& Henshaw,J. (2018). Historical evolution of regulatory standards for occupational and consumer exposures to industrial talc. Regulatory Toxicology and Pharmacology, 92, 251–267. Recuperado de https://doi.org/10.1016/j.yrtph.2017.12.005.
Earnhart, D., & Leonard, J. (2016). Environmental audits and signaling: The role of firm organizational structure. Resource and Energy Economics, 44(16), 1-22.
Ebrahimi,A., Jeon,H., & Leeb, C. (2020). Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms. Computers & Industrial Engineering , 141, 1-21. Recuperado https://doi.org/10.1016/j.cie.2020.106295.
Elkington, J. (1994). Towards the sustainable corporation: win-win-win business strategies for sustainable development. Calif. Manag. Rev, 36, 90 - 100.
Elkington, J. (1998). Partnerships from Cannibals with Forks: the triple bottom line of 21st-century business. Environ. Qual. Manag, 8(11), 37- 51.
EPA. (2017). Agencia de Proteccion Ambiental de los Estados Unidos. Descripción general de los gases de efecto invernadero. Obtenido de https://www.epa.gov/ghgemissions/overview-greenhouse-gases
Ervurala, B., Zaima,S., Demireld, O., Aydinb,Z., & Dursun,D. (2018). An ANP and fuzzy TOPSIS-based SWOT analysis for Turkey’s energy planning. Renewable and Sustainable Energy Reviews, 82, 1538–1550.
FACTS. (2015). Agencia Europea para la Seguridad y la Salud en el Trabajo. Los efectos del ruido en el trabajo. España.
Fazel Zarandi, M.H., Sadat Asl, A.A., Sotudian, S., Castillo, O., 2020. A state of the art review of intelligent scheduling, Artificial Intelligence Review. Springer Netherlands, 53, 501-593. https://doi.org/10.1007/s10462-018-9667-6
Fadil,A., Chetouane,F & Binder,Z. (1997). Control of flexible job-shop: disturbances and robustness. IFAC Management and Control of Production and Logistics, 13-18.
Famiyeh, S., Kwarteng, A., Asante-Darko, D., y Dadzie, S. A. (2018). Green supply chain management initiatives and operational competitive performance. Benchmarking, 25(2), 607–631. https://doi.org/10.1108/BIJ-10-2016-0165
Fasecolda. (2020). Reporte por clase de riesgo y actividad económica - Consolidado. Obtenido de https://sistemas.fasecolda.com/rldatos/Reportes/xClaseGrupoActividad.aspx
Ferreira,V., Pinto,O., Arroyo,R., & Castro,E. (2018). Berry color variation in grapevine as a source of diversity. Plant Physiology and Biochemistry, 132, 696–707. Recuperado de https://doi.org/10.1016/j.plaphy.2018.08.021.
Fitzpatrick, J. (2016). Environmental sustainability assessment of using forest wood for heat energy in Ireland. Renewable and Sustainable Energy Reviews, 57, 1287–1295. Recuperado de http://doi.org/10.1016/j.rser.2015.12.197.
Fleming,J. (1985). Computer aided design of regulators using multiobjective optimization. Annual Review in Automatic Programming, 13(2), 47-52. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.
Gao, J., Gen, M., & Sun, L. (2007). Scheduling jobs and maintenances in flexible job shop with a hybrid genetic algorithm. Journal of Intelligent Manufacturing, 17(4), 493–507. Recuperado de http://doi.org/10.1007/s10845-005-0021-x.
Gao, K., Suganthan, P., Pan, Q., Chua, T., Cai, T., & Chong, C. (2014). Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling. Information Sciences, 289(67), 76–90. Recuperado de http://doi.org/10.1016/j.ins.2014.07.039.
Gao, K., Suganthan, P., Pan, Q., Chua, T., Chong, C., & Cai, T. (2016a). An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time. Expert Systems with Applications, 65, 52-67. doi: 10.1016/j.eswa.2016.07.046.
Gao, L., Zhou, B., Yang, L., & Wang, J. (2015). A multi-objective integrated optimization method for fjsp based on multi-rule resource allocation. Journal of Shanghai jiaotong University, 49(08), 1191–1198. Recuperado de http://xuebao.sjtu.edu.cn/CN/abstract/abstract10626.shtml.
Gao,J., Gen,M., Sun,L., & Zao,X. (2007a). A hybrid of genetic algorithm and bottleneck shifting for multiobjective flexible job shop scheduling problems. Computers & Industrial Engineering, 53, 149–162.
Gao,K., Suganthan,P., Chua,T., Chong,C., Cai,T., & Pan, Q. (2015a). A two-stage artificial bee colony algorithm scheduling flexible job-shop scheduling problem with new job insertion. Expert Systems with Applications , 42, 7652–7663.
Gao,K., Suganthan,P., Pan,Q., Tasgetiren,M., & Sadollah,A. (2016b). Artificial bee colony algorithm for scheduling and rescheduling fuzzy flexible job shop problem with new job insertion. Knowle dge-Base d Systems, 109, 1–16.
Gao,K., Suganthan,P., Tasgetiren,M., Pan,Q., & Sun,Q. (2015b). Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion. Computers & Industrial Engineering , 90, 107–117.
Gao,Z., Sun,D., Zhao, R., & Dong, Y. (2021). Ship-unloading scheduling optimization for a steel plant. Information Sciences, 544, 214–226.
Gao,L.,& Pan,Q. (2016). A shuffled multi-swarm micro-migrating birds optimizer for a multi-resource-constrained flexible job shop scheduling problem. Information Sciences , 372, 655–676.
Garcia,S., Cintra,Y., Torres,C ., Guasti,F. (2016). Corporate sustainability management: a proposed multi-criteria model to support balanced decision-making. Journal of Cleaner Production, 136 (2016) 181-196. Recuperado de http://dx.doi.org/10.1016/j.cor.2016.03.009.
Geoffrion, A., Dyer, J., & Feinberg, A. (1972). An interactive approach for multi-criterion optimization, with an application to the operation of an academic department. Management Science, 19(4), 357-368. Recuperado de https://doi.org/10.1287/mnsc.19.4.357.
Geyik,F., & Dosdog˘ru,T. (2013). Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach. Neural Comput & Applic, 23, 1631–1641.doi: 10.1007/s00521-012-1119-7.
Ginting,R., Wanli., & Malik,A. (2020). Crude Palm Oil Product Quality Control Using Seven Tools (case study:XYZ Company). IOP Conf. Series: Materials Science and Engineering, 1-9, doi:10.1088/1757-899X/851/1/012046.
Giraldo, J.A., Castrillón, O.D., & Ruiz, S. (2019). Discrete Simulation and Agents of a Simple Chain Supply including a Geographic Information System (GIS). . Revista Información Tecnológica, 30(6), 123-136. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000600123.
Giret, A., Trentesaux, D., & Prabhuc, V., (2015). Sustainability in manufacturing operations scheduling: a state of the art review. J. Manuf. Syst. 37, 126e140.
Glover,F & Kochenberger,G. (2010). Handbook of Metaheuristics. Boston: Springer. Recuperado de http://doi.org/10.1007/978-1- 4419-1665-5.
Goldberg,D. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison-Wesley Longman Publishing.
Gómez, P. (2010). Programación de la producción en un taller de flujo híbrido sujeto a incertidumbre: arquitectura y algoritmos. España: Universidad Politécnica de Valencia.
Gong,G., Deng,Q., Gong,X., Liu, W., & Ren,Q. (2018). A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators. Journal of Cleaner Production, 174, 560- 576. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.188.
Gong,X., Deng,Q., Gong,G., Liu,W., & Ren,Q. (2017). A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility. International Journal of Production Research, 56 (7)., 2506–2522, Recuperhttps://doi.org/10.1080/00207543.2017.1388933.
Gong,V., Van der Wee,M., De Pessemier,T., Verbrugge,S., Colle,D., & Martens,L. (2017a). Energy- and labor-aware production scheduling for sustainable manufacturing: A case study on plastic bottle manufacturing. 24th CIRP Conference on Life Cycle Engineering, 2212-8271, doi: 10.1016/j.procir.2016.11.136.
Gracia,J.,Lara,L., Quintero,D., & Santis,A. (2018). Formulation of Strategies for the Implementation of Integral 14001:2015 in the Company Surtiapliques (Bogotá-Colombia). Chemical Engineering Transactions, 12, 559-564. doi: 10.3303/CET1867094.
Grossmann, E., Halemane, K., & Swaney, R. (1983). Optimization strategies for flexible chemical processes. Computers and Chemical Engineering, 7(4), 439-462. Recuperado de https://doi.org/10.1016/0098-1354(83)80022-2.
Gu,X., Huang,M., & Liang, X. (2020). A Discrete Particle Swarm Optimization Algorithm With Adaptive Inertia Weight for Solving Multiobjective Flexible Job-shop Scheduling Problem. IEEE Access, 8, 33125- 33136.
Guía País - Kenia. (2012). España: Ministerio de Asuntos Exteriores y Cooperación .
Gupta, S.M., 2016. Lean manufacturing, green manufacturing and sustainability. J. Japan Ind. Manag. Assoc. 67, 102–105. https://doi.org/10.11221/jima.67.102
Gutiérrez,C & García,I. (2011). Modular design of a hybrid genetic algorithm for a flexible job–shop scheduling problem. Knowledge-Based Systems, 24, 102–112. doi:10.1016/j.knosys.2010.07.010.
Hadka,D., & Reed,P. (2012). Diagnostic Assessment of Search Controls and Failure Modes in Many-Objective Evolutionary Optimization. Evolutionary Computation, 20(3), 423–452.
Haitham,S.,& and Deb,H. (2014). U-NSGA-III: A Unified Evolutionary Algorithm for Single,Multiple, and Many-Objective Optimization. Computational Optimization and Innovation Laboratory, 5(12), 1-30.
Hale,J., Legun,J., Campbell,H.,& Carolan,M. (2019). Social sustainability indicators as performance. Geoforum , 103, 47–55. Recuperado de https://doi.org/10.1016/j.geoforum.2019.03.008.
Haugeland, J. (1985). Artificial Intelligence: The Very Idea. Cambridge: MIT Press.
He, W., & Sun, D. (2012). Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies. . The International Journal of Advanced Manufacturing Technology, 66(1-4), 501–514. http://doi.org/10.1007/s00170-012-4344-4.
He,Y., Li,Y., Wu,T., Sutherland,J. (2015). An energy-responsive optimization method for machine tool selection and operation sequence in flexible machining job shops. Journal of Cleaner Production, 87, 245-254. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2014.10.006.
Herrero,M.,& Stuckey,D. (2015). Bioaugmentation and its application in wastewater treatment: A review. Chemosphere, 140 , 119–128. Recuperado de http://dx.doi.org/10.1016/j.chemosphere.2014.10.033.
Holcombe, H. (2018). Queer After All: Middlesex, the Ford Factory, and the Genetic History of Incest. Arizona Quarterly. Journal of American Literature, Culture, and Theory , 74(2), 1-37. doi:10.1353/arq.2018.0007.
Holland, J. H. (1975). Adaptation in natural and artificial systems.The University of Michigan Press, Englewood Cliffs, 66–72.
Hongbo,L.,, Ajith,A., Zuwen,W. (2009). A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems. Fundamenta Informaticae , 95 (4), 465-489.
Hongfeng,W., Yaping,F., Min,H., George,Q ., Huang,B ., Junwei,W. (2017). A NSGA-II based memetic algorithm for multiobjective parallel flowshop scheduling problem. Computers & Industrial Engineering, 113, 185–194. Recuperado de http://dx.doi.org/10.1016/j.cie.2017.09.009.
Hongtao,T., Rong,C,, Yibing,L., Zhao,P-. Shunsheng,G.,&Yuzhu,D. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, 176–194. Recuperado de https://doi.org/10.1016/j.asoc.2019.02.011.
Hormozi,H., Zhang, Z., Zarei,O., & Ramezanian, R. (2018). Trade-off between the costs and the fairness for a collaborative production planning problem in make-to-order manufacturing. Computers & Industrial Engineering, 126, 421-434. recuperado de https://doi.org/10.1016/j.cie.2018.09.044.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, Orlando, FL, 82-87. doi: 10.1109/ICEC.1994.350037.
Hossain,S., Galbreath.J., Monzur,M., & Randøy,T. (2020). Does competition enhance the double-bottom-line performance of microfinance institutions? Journal of Banking and Finance 113 (2020) 105765, 113, 1-17. Recuperdo de https://doi.org/10.1016/j.jbankfin.2020.105765.
Hosseinabadi, R., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2014). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterpris. Annals of Operations Research, 229(1), 451-474. doi: 10.1007/s10479-014-1770-8.
Hu,C., Dai, L., Yan,X., Gong,W., Liu,X., & Wang,L. (2020). Modified NSGA-III for sensor placement in water distribution system. Information Sciences, 509, 488–500. Recuperado de https://doi.org/10.1016/j.ins.2018.06.055.
Huang, J., & Süer, G. (2015). A dispatching rule-based genetic algorithm for multi-objective job shop scheduling using fuzzy satisfaction levels. Computers & Industrial Engineering, 86, 29–42. Recuperado de http://doi.org/10.1016/j.cie.2014.12.001.
Huang, R., Yang, C., & Cheng, W. (2013). Flexible job shop scheduling with due window-a two-pheromone ant colony approach. International Journal of Production Economics, 141(2), 685–697. Recuperado de http://doi.org/10.1016/j.ijpe.2012.10.011.
Huang, S., Tian, N., Wang, Y., & Ji, Z. (2016a). Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization. Springer Plus, 5 (1). doi: 10.1186/s40064-016-3054-z.
Huang, W., Zhao. Y. & Ma, X. (2014). An improved genetic algorithm for job-shop scheduling problem with process sequence flexibility. International . Journal of Simulation Modeling, 13(4), 510-522. Recuperado de http://m.ijsimm.com/Full_Papers/Fulltext2014/text.
Huang, X., & Yang, L. (2019). A hybrid genetic algorithm for multi-objective flexible job shop scheduling problem considering transportation time. International Journal of Intelligent Computing and Cybernetics, 12 (2), 154-174.
Huang,D.,Tian,N.,& Ji, W. (2016b). Particle swarm optimization with variable neighborhood search for multiobjective flexible job shop scheduling problem. International Journal of Modeling, Simulation, and Scientific Computing, 7(3). Recuperado de https://doi.org/10.1142/S1793962316500240
Hui,L., Rongrong,Z., , Shi,C., & Yuhui,S. (2019). Multi-center variable-scale search algorithm for combinatorial optimization problems with the multimodal property. Applied Soft Computing Journal, 84, 105-116. Recuperado de https://doi.org/10.1016/j.asoc.2019.105726.
Huot, H., Séré, G., Charbonnier, P., Simonnot, M., & Morel, J. (2015). Lysimeter monitoring as assessment of the potential for revegetation to manage former iron industry settling ponds. The Science of the Total Environment, 526, 29–40. Recuperado de http://doi.org/10.1016/j.scitotenv.2015.04.025.
Hutchins,M., & Sutherland,J. (2008). An exploration of measures of social sustainability and their application to supply chain decisions. Journal of Cleaner Production, 16 (15), 1688-1698. Recuperado de https://doi.org/10.1016/j.jclepro.2008.06.001.
Hutchins,M., Richter,J., Henry,M., & Sutherland,J. (2019). Development of indicators for the social dimension of sustainability in a U.S. business context. Journal of Cleaner Production , 212, 687- 697. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.199.
Hutomo, M; Haizam., & Sinaga. (2018). The Mediating Role of Organizational Learning Capability On Green Distribution and Green Packaging TowardsSustainability Performance as A Function Environmental. Dynamism: Indonesia and Malaysia Fishery Industries. Conf. Series: Earth and Environmental Science , 164,1-12.
Ibrahim,A., Vargas,M., Rahnamayan,S., & Deb,K. (2016). EliteNSGA-III: An Improved Evolutionary Many-Objective Optimization Algorithm. WCCI, 11(7),1-10.
IEA. (2019). Emisiones globales de CO2 en 2019. Obtenido de https://www.iea.org/articles/global-co2-emissions-in-2019
Ishibuchi,H., & Murata. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 28(3), 392-403. doi: doi: 10.1109/5326.704576.
Jain,V., Singhal,A., Sachdeva,G., & Kachhwaha. (224 (2020) 113348). Advanced exergy analysis and risk estimation of novel NH3-H2O and H2O-LiBr integrated vapor absorption refrigeration system. Energy Conversion and Management, 224, 2-16.
Jalali, T., Jafari,M.,& Mohammadi,A. (2018). Genetic algorithm optimization of antireflection coating consisting of nanostructured thin films to enhance silicon solar cell efficacy. Materials Science & Engineering B , 247, 1-8. Recuperado de https://doi.org/10.1016/j.mseb.2019.05.016.
Jarke,M., & Radermacher,F. (1988). The AI potential of model management and its central role in decision support. Decision Support Systems, 387-404, Recuperado de https://doi.org/10.1016/0167-9236(88)90002-4.
Jeyadebi,S., Baskar,S., Babula,S., & Iruthayarajan,W. (2011). Solving multiobjective optimal reactive power dispatch using modified NSGA-II. International Journal of Electrical Power & Energy Systems, 33(2), 219-228, Recuperado de https://doi.org/10.1016/j.ijepes.2010.08.017.
Jia, S., & Hu, Z. (2014). Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem. . Computers & Operations Research, 47, 11–26. Recuperado de http://doi.org/10.1016/j.cor.2014.01.010.
Jia, Y., & Liu, R. (2012). Study of the energy and environmental efficiency of the Chinese economy based on a DEA model. Procedia Environmental Sciences, 13, 2256–2263. Recuperado de http://doi.org/10.1016/j.proenv.2012.01.214.
Jia, Z., Zhu, J., & Chen, H. (2012). Dynamic tabu particle swarm optimization algorithm for flexible job-shop scheduling. Journal of South China University of Technology, 40(1). Recuperado de https://scholar.google.es/scholar?q=Dynamic+tabu+particle+swarm+optimization+algorithm+for+flexible+job-shop+scheduling++&btnG=&hl=en&as_sdt=0%2C5#0.
Jia,F., Yin,S., Chen,L.,& Chen,X. (2020). The circular economy in the textile and apparel industry: A systematic literature review. Contents lists available at Science Direct, 12(21), 1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120728.
Jia,Z., Pei,M & Leung,J. (2017). Multi-objective ACO algorithms to minimise the makespan and the total rejection cost on BPMs with arbitrary job weights. International Journal of Systems Science, 48(16), 3542-3557. Recuperado de https://doi.org/10.1080/00207721.2017.1387314.
Jiang, Z., Zuo, L., & E, M. . (2014). Study on multi-objective flexible job-shop scheduling problem considering energy consumption. Journal of Industrial Engineering and Management, 7(3), 589–604. Recuperado de http://doi.org/10.3926/jiem.1075.
Jiang,E., & Wang,L. (2020). Multi-objective optimization based on decomposition for flexible job shop scheduling under time-of-use electricity prices. Knowledge-Based Systems, 204, 106- 177.
Jiang,M., Zeng,M., Cao, Z.,Bi,Y., Wang, L., Wang,Y. (2019). The history, logic and trends of the discipline of safety science in China. Safety Science , 116, 37–148. Recuperado https://doi.org/10.1016/j.ssci.2019.03.005.
Jiang,T & Deng,G. (2018). Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption. IEEE Access, 6, 46346- 46354. doi:0.1109/ACCESS.2018.2866133.
Jiao,Z., Yuan, S., Ji,C., Mannan,S., & Wang,Q. (2019). Optimization of dilution ventilation layout design in confined environments. Journal of Loss Prevention in the Process Industries, 60, 195–202, Recuperado de https://doi.org/10.1016/j.jlp.2019.05.002.
Joc, T., & Nhu, H. (2008). Evolving dispatching rules using genetic programming for solving multiobjective flexible job shop problems. Computers & Industrial Engineering , 54 (22), 453–473.
Jones,J., Bleasdale,S., Maita,D., & Brosseau,L. (2020). A systematic risk-based strategy to select personal protective equipment for infectious diseases. American Journal of Infection Control, 48, 46−51. Recuperado de https://doi.org/10.1016/j.ajic.2019.06.023.
Kacem, I., Hammadi, S., & Borne, P. (2002). Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 60(3-5), 245–276. recuperado de http://doi.org/10.1016/S0378-4754(02)00019-8.
Kaplanoğlu, V. (2016). An object-oriented approach for multi-objective flexible job-shop scheduling problem. Expert Systems with Applications, 45, 71–84. http://doi.org/10.1016/j.eswa.2015.09.050, 45, 71–84.
Karimi-Nasab, M., Modarres, M., & Seyedhoseini, S. (2015). A self-adaptive PSO for joint lot sizing and job shop scheduling with compressible process times. Applied Soft Computing, 27, 137–147. http://doi.org/10.1016/j.asoc.2014.10.015, 27, 137–147. Recuperado de http://doi.org/10.1016/j.asoc.2014.10.015.
Karthikeyan, S., Asokan, P., & Chandrasekaran, M. (2014a). A Hybrid Discrete Firefly Algorithm for Multi-Objective Flexible Job Shop Scheduling Problems with Maintenance Activity. Applied Mechanics and Materials, 575, 922–925. http://doi.org/10.4028/www.scientific.net/AMM.575.922.
Karthikeyan, S., Asokan, P., & Nickolas, S. (2014b). A hybrid discrete firefly algorithm for multi-objective flexible job shop scheduling problem with limited resource constraints. The International Journal of Advanced Manufacturing Technology, 72 (9), 9-12. Obtenido de http://doi.org/10.1007/s00170-014-5753-3.
Kay Camarillo, M., Stringfellow, W. T., Jue, M. B., & Hanlon, J. . (2012). Economic sustainability of a biomass energy project located at a dairy in California, USA. . Energy Policy, 48, 790–798. Recuperado de http://doi.org/10.1016/j.enpol.2012.06.020.
Kaya, S. & Figlali, N. (2013). Multi Objective Flexible Job Shop Scheduling Problems. Journal of Engineering and Natural Sciences, 31, 605-623. Recuperado de http://eds.yildiz.edu.tr/ArticleContent/Journal/sigma/Volumes/2013/Issues/Regular-4/YTUJENS-20134/YTUJENS-2013-31-4.485.pdf .
Kempen,E., Casas,M., Pershagen,G., & Foraster,M. (2018). WHO Environmental Noise Guidelines for the European Region: A Systematic Review on Environmental Noise and Cardiovascular and Metabolic Effects: A Summary. International Journal of Environmental Research and Public Health, 15 (379), 1.59. doi:10.3390/ijerph15020379.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks, 4, 1942–1948.
Khan,I. (2020). Critiquing social impact assessments: Ornamentation or reality in the Bangladeshi electricity infrastructure sector? Energy Research & Social Science , 60 (10), 1-15. Recuperado de https://doi.org/10.1016/j.erss.2019.101339.
Khosravi,F.,& Izbirak,G. (2019). A stakeholder perspective of social sustainability measurement in healthcare. Sustainable Cities and Society , 50, 1-12. Recuperado de https://doi.org/10.1016/j.scs.2019.101681.
Kiourtisa,A.,Nifakosb,S., Mavrogiorgoua,A., & Kyriazisa,D. (2019). Aggregating the syntactic and semantic similarity of healthcare data towards their transformation to HL7 FHIR through ontology matching. International Journal of Medical Informatics, 132, 1-16. Recuperado de https://doi.org/10.1016/j.ijmedinf.2019.104002.
Korhonen, P.., & Laakso, J. (1986). A visual interactive method for solving the multiple criteria problem (VIA). European Journal of Operational Research, 24(2), 277-287. Recuperado de https://doi.org/10.1016/0377-2217(86)90050-0.
Kroppa,I., Nejadhashemia,A.,Deb,K., Aboualia,M., Royc,P., Adhikaria, U., & Hoogenboomd,G. (2019). A multi-objective approach to water and nutrient efficiency for sustainable agricultural intensification. Agricultural Systems, 173, 289–302. Recuperado de https://doi.org/10.1016/j.agsy.2019.03.014.
Kudryavtsev,S., Yemelin,P., & Yemelina,N. (2018). The Development of a Risk Management System in the Field of Industrial Safety in the Republic of Kazakhstan. Safety and Health at Work, 9, 30-41. Recuperado de http://dx.doi.org/10.1016/j.shaw.2017.06.003.
Kumar,A., & Anbanandam,R. (2019). Development of social sustainability index for freight transportation system. Journal of Cleaner Production, 210, 77- 92. Recuperado de https://doi.org/10.1016/j.jclepro.2018.10.353.
Kvam,P., & Hintze,A. (2018). Rewards, risks, and reaching the right strategy: Evolutionary paths from heuristics to optimal decisions. Research Gate, 1-13. Recuperado de https://www.researchgate.net/publication/321106854.
Kyoung,M., Jin,Y. (2017). Exposure to environmental noise and risk for male infertility: A population-based cohort study. Environmental Pollution, 226: 118. doi 10.1016/j.envpol.2017.03.069.
Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, 143(1), 24–34. recuperado de http://doi.org/10.1016/j.ijpe.2010.07.012.
Laosiritaworn,W., Pinchai, N., & Parupuncharadsri,N. (2020). Job Shop Layout Improvement with Simulation Technique: A Case of. IOP Conference Series: Materials Science and Engineering, 1-6.
Laukkanen,M. (2019). Sustainable business models for advancing system-level sustainability. London: Acta Universitatis.
Lavicoli,A., Gambelunghe , A., Magrini, G., Mosconi d, L., Soleo, L., Vigna, A., Trevisan, A., Bruno, M., Chiambretti,M., Scarpitta, L., Sciacca, U & Valentini. (2019). Diabetes and work: The need of a close collaboration between diabetologist and occupational physician. Nutrition, Metabolism & Cardiovascular Diseases, 29, 220-227. Recuperado de https://doi.org/10.1016/j.numecd.2018.10.012.
Lei, D., & Guo, X. (2015). An effective neighborhood search for scheduling in dual-resource constrained interval job shop with environmental objective. International Journal of Production Economics, 159, 296–303. Recuperado de http://doi.org/10.1016/j.ijpe.2014.07.026.
Lei,D., Li,M., & Wang,L. (2019). A Two-Phase Meta-Heuristic for Multiobjective Flexible Job Shop Scheduling Problem With Total Energy Consumption Threshold. IEEE Transactions on Cybernetics , 49(3), 1097-1109,doi: 10.1109 / TCYB.2018.2796119.
Leyland, G. (2002). Multi-objective optimisation applied to Industrial Energy Problems. Auckland - Nueva Zelanda: Universidad de Auckland.
Li, C., Cui, H., & Wang, G. (2013a). The Optimization of Flexible Job-Shop Scheduling Problem Based on NSGA-II. Advanced Materials Research, 651(3), 684-687. Recuperado de http://www.scientific.net/AMR.651.684.
Li, J., & Pan, Q. (2013). Chemical-reaction optimization for solving fuzzy job-shop scheduling problem with flexible maintenance activities. International Journal of Production Economics, 145(1), 4–17. Recuperado de http://doi.org/10.1016/j.ijpe.2012.11.005.
Li, J., Duan, P., Cao, J., X., Lin,X.,& Han,Y. (2018a). A Hybrid Pareto-Based Tabu Search for the Distributed Flexible Job Shop Scheduling Problem With E/T Criteria. IEEE Access, 58883-58897, doi: 10.1109/ACCESS.2018.2873401.
Li, J., Pan, Q., & Liang, Y. (2010a). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59(4), 647–662. Recuperado de http://doi.org/10.1016/j.cie.2010.07.014.
Li, J., Pan, Q., & Xie, S. (2012a). An effective shuffled frog-leaping algorithm for multi-objective flexible job shop scheduling problems. Applied Mathematics and Computation, 218(18), 9353–9371. Recuperado de http://doi.org/10.1016/j.amc.2012.03.018.
Li, J.-Q., Pan, Q.-K., & Chen, J. (2012b). A hybrid Pareto-based local search algorithm for multi-objective flexible job shop scheduling problems. International Journal of Production Research, 50(4), 1063–1078. Recuperado de http://doi.org/10.1080/00207543.2011.555427.
Li, J.-Q., Pan, Q.-K., & Tasgetiren, M. (2014). A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities. Applied Mathematical Modelling, 38(3), 1111–1132. Recuperado de http://doi.org/10.101.
Li, L., & Huo, J. (2009). Multi-Objective Flexible Job-Shop Scheduling Problem in Steel Tubes Production. Systems Engineering - Theory & Practice, 29(8), 117–126. Recuperado de http://doi.org/10.1016/S1874-8651(10)60063-4.
Li,B., Guo, C.,& Ning, T. (2018b). An improved bacterial foraging optimization for multi-objective flexible job-shop scheduling problem(Article). Journal Europeen des Systemes Automatises, 51(8),323-332.
Li,H., Zhu,H., & Jiang,T. (2020a). Modified Migrating Birds Optimization for Energy-Aware Flexible Job Shop Scheduling Problem. Algorithms , 23(12), 13-44. recuperado de doi:10.3390/a13020044.
Li,J., Deng,J., Li,C., Han,Y., Tian,J., Zhang,B.,& Wang,C. (2020b). An improved Jaya algorithm for solving the flexible job shop scheduling problem with transportation and setup times. Knowledge-Based Systems, 200.1-12. Recuperado de https://doi.org/10.1016/j.knosys.2020.106032.
Li, J., & Pan, Q. (2012). Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity. Applied Soft Computing, 12(9), 2896–2912. Recuperado de http://doi.org/10.1016/j.asoc.2012.04.012.
Li, J., Pan,Q., & Gao,K. (2011a). Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems. Int J Adv Manuf Technol, 4(5), :1159–1169.
Li, J., Paq,Q., Xie,S., Wang,S. (2011b). A Hybrid Artificial Bee Colony Algorithm for Flexible Job Shop Scheduling Problems. Int. J. of Computers, Communications & Control, 6(2), 286-296.
Li, J., Pan,Q., Wang,F. (2014a). A hybrid variable neighborhood search for solving the hybrid flowshop scheduling problem. Applied Soft Computing , 24(12), 63–77.
Li,U.,Pan,Q., Wang, Y. (2010b). Hybrid Pareto-based tabu search algorithm for solving the multi-objective flexible Job Shop scheduling problem. Computer Integrated Manufacturing Systems, 16(7), 1419-1426.
Li,X., Peng,Z., Dub, B., Guo,J., Xu,W., Zhuang, K . (2016). Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Computers & Industrial Engineering, 113 (2017) 10–26. Recuperdo de http://dx.doi.org/10.1016/j.cie.2017.09.005.
Li,X.,& Gao,L. (2016). An effective hybrid genetic algorithm and tabu searchfor flexiblejob shop schedulingproblem. Int. J.Production Economics, 174, 93–110. Recuperado http://dx.doi.org/10.1016/j.ijpe.2016.01.016.
Li,Y., He,Y., Wang,Y.,Tao,F., & Sutherland,J. (2020c). An optimization method for energy-conscious production in flexible machining job shops with dynamic job arrivals and machine breakdowns. Journal of Cleaner Production , 254, 1- 14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120009.
Li,Z., Qian,B., Hu,R., Chang,L.,& Yang,J. (2019). An elitist nondominated sorting hybrid algorithm for multi-objective flexible job-shop scheduling problem with sequence-dependent setups. Knowledge-Based Systems, 173, 83–112.
Lin,J., Pan,Q., & Liang,Y. (2010). An effective hybrid tabu search algorithm for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 59, 647–662.
Liu, A., Ya, Yang., Xing, Q., & Lu, H. (2011a). Dynamic scheduling on multi-objective flexible Job Shop. Computer Integrated Manufacturing Systems, 17(12), 2629-2637.
Liu, A., Ya, Yang., Xing, Q., Lu, H., & Zhang, Y. (2011b). Multi-population genetic algorithm in multi-objective fuzzy and flexible Job Shop scheduling. Computer Integrated Manufacturing Systems,17(9), 1954-1961.
Liu, B., Fan, Y., & Liu, Y. (2015a). A fast estimation of distribution algorithm for dynamic fuzzy flexible job-shop scheduling problem. Computers & Industrial Engineering, 87, 193–201. Recuperado de http://doi.org/10.1016/j.cie.2015.04.029 .
Liu, Q., Tian,Y., Wang, C., Chekem, F., & Sutherland, J. (2018). Flexible job-shop scheduling for reduced manufacturing carbon footprint. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 140(6), 21-39.
Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2015b). Reducing environmental impact of production during a Rolling Blackout policy – A multi-objective schedule optimisation approach. Journal of Cleaner Production, 102, 418–427. Recuperado de http://doi.org/10.1016/j.jclepro.2015.04.038.
Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetical objective genetic algorithm for optimisation of energy consumption and shop floor production performance. Int. J. Production Economics, 179(2016)259–272. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.
Liu,D., Huang,Q., Yang,Y., Liu, D.,& Wei,X. (2020). Bi-objective algorithm based on NSGA-II framework to optimize reservoirs operation. Journal of Hydrology, 585, 1-12. Recuperado de https://doi.org/10.1016/j.jhydrol.2020.124830.
Liu,H., Chena,C., Lv,X., Wuc,X ., & Liud,M. (2019). Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management, 195 , 328–345. Recuperado de https://doi.org/10.1016/j.enconman.2019.05.020.
Liu,N., Zhang,Y., & Feng,W. (2019b). Improving Energy Efficiency in Discrete Parts Manufacturing System Using an Ultra Flexible Job Shop Scheduling Algorithm. International Journal of Precision Engineering and Manufacturing-Green Technology, 6:349–365. recuperado de https://doi.org/10.1007/s40684-019-00055-y.
Liu,Q., Zhan,M .,Chekem,F.,Shao,X .,Ying,B., & Sutherland,J. (2017). A hybrid fruit fly algorithm for solving flexible job-shop scheduling to reduce manufacturing carbon footprint. Journal of Cleaner Production, 168, 668-678. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.09.037.
Liu,T., Chen,Y.,& Chou,H. (2014). Developing a Multiobjective Optimization Scheduling System for a Screw Manufacturer: A Refined Genetic Algorithm Approach. IEEE Aceess, 2(11),356- 364.
Liu,Y.,Dong,H., Lohse,N., & Petrovic,S. (2016). A multi-objective genetic algorithm fo roptimisation of energy consumption and shop floor production performance. Int. J.ProductionEconomics, 179(, 259–272. Obtenido de http://dx.doi.org/10.1016/j.ijpe.2016.06.019.
Liu, Z., Guo,S., & Wang,L. (2019a). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765- 786. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.231.
Liu, Z., Yan,J., Cheng,Q., Yang,C., & Sun,S. (2020). The mixed production mode considering continuous and intermittent processing for an energy-efficient hybrid flow shop scheduling. Journal of Cleaner Production , 246, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119071.
Liu, Q., Panb,Q., Gaoa,L., & Lia,X. (2019). Multi-Objective Flexible Job Shop Scheduling Problem Considering Machine Switching Off-On Operation. Procedia Manufacturing, 39, 1167–1176.
López, M., Díaz Flores.,& Winkler,R. (2018). Increase of peroxidase activity in tropical maize after recurrent selection to storage pest resistance. Journal of Stored Products Research, 75, 47- 55. Recuperado de https://doi.org/10.1016/j.jspr.2017.11.007.
Lozano, S., & Calzada, L. (2019). Efficiency ranking using dominance network and multiobjective optimization indexes. Expert Systems with Applications, 126, 83-91. Recuperado de https://doi.org/10.1016/j.eswa.2019.02.016.
Lu, C., Li, X., Gao, L., Liao, W., & Yi, J. (2017). An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times. Computers & Industrial Engineering , 104, 156–174. Recuperado de http://dx.doi.org/10.1016/j.cie.2016.12.020.
Lu, K., Ting, L., Keming, W., Hanbing, Z., Makoto, T., & Bin, Y. (2015). An Improved Shuffled Frog-Leaping Algorithm for Flexible Job Shop Scheduling Problem. Algorithms. Algorithms, 8(1), 19-3. http://www.mdpi.com/1999-4893/8/1/19/htm.
Lu, Y., & Zhang, X. (2015). Corporate sustainability for architecture engineering and construction (AEC) organizations: Framework, transition and implication strategies. Ecological Indicators, 61, 911–922. http://doi.org/10.1016/j.ecolind.2015.10.046.
Luger, F & Stubblefield, W. (1993). Artificial Intelligence Structures and Strategies for Complex Problem Solving. Palo Alto: Benjamin Cummings.
Luger, G. (2002). Artificial Intelligence: Structures and Strategies for Complex Problem Solving . London: Addison-Wesley Longman.
Lui, Q., Li, X., Liu, H., & Guo, Z. (2020). Multi-objective metaheuristics for discrete optimization problems: A review of the state-of-the-art. . Applied Soft Computing Journal, , 93, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2020.106382.
Luo,D., Chen,H.,Wu,S., Shi, Y. (2010). Hybrid ant colony multi-objective optimization for flexible job shop scheduling problems. Journal of Internet Technology, 11(3), 361-370.
Luo,S., Zhang,L., & Fan,Y. (2019). Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. Journal of Cleaner Production 234 (2019), 1365-1384. Recuperado de https://doi.org/10.1016/j.jclepro.2019.06.151.
Macias,S., & Sakao,T. (2020). Effective ecodesign implementation with the support of a lifecycle engineer. Journal of Cleaner Production, 279, 1-10.
Magazzino, C., Mele, M., Schneider, N., & Sarkodie, S.A., 2020. Waste generation, Wealth and GHG emissions from the waste sector: Is Denmark on the path towards Circular Economy? Sci. Total Environ. 755, 142510. https://doi.org/10.1016/j.scitotenv.2020.142510
Maes,P. (1993). Designing Autonomous Agents: Theory and Practice from Biology to Engineering and Back. London: Firet MIT Press edition .
Mahmud,M.,Abidina,A., Mohamed,Z.,Rahman,M., & Lida,M. (2019). Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Computers and Electronics in Agriculture, 157, 488-499. recuperado de https://doi.org/10.1016/j.compag.2019.01.016.
Mahzun, R., & Kalalo, F. (2019). The environmental aspect and impact assessment for heavy industries: Empirical study on steel fabrication and shipyard operations in batam Indonesia. Quality - Access to Success, 20 (172), 108-113.
Maiera,H., Razavib,S., Kapeland,Z., Matotte,L., & Kasprzykf, B. (2019). Introductory overview: Optimization using evolutionary algorithms and other metaheuristics. Environmental Modelling and Software , 114 (, 195–213. Recuperado de https://doi.org/10.1016/j.envsoft.2018.11.018.
Malek,J & Desai,T. (2020). A systematic literature review to map literature focus of sustainable manufacturing. Journal of Cleaner Production, 256,1-20. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120345.
Mandal,P., Perumal,G., Arora,H., & Ghosh,S. (2020). Green manufacturing of nanostructured Al-Based sustainable selfcleaning metallic surfaces. Journal of Cleaner Production, 278, 1-10.
Mani, V., Agrawal, R., & Sharma, V. (2015). Social sustainability practices in the supply chain of Indian manufacturing industries. International Journal of Automation and Logistics,, 1(3), 211 - 233.
Mani,V., Chiappetta,C.,& Mani,J. (2020). Supply chain social sustainability in small and medium manufacturing enterprises and firms’ performance: Empirical evidence from an emerging Asian economy. Int. J. Production Economics , 227, 1-13.
Maqrini,H & Teghem,H. (1995). Scheduling complex flexible job shop problems. Proceedings INRIA/IEEE Symposium on Emerging Technologies and Factory Automation. ETFA'95, Paris, France, 541-549, doi: 10.1109/ETFA.1995.496806.
Marimin, M., & Farhan, M. (2020). Sustainable flexible flow shop scheduling optimization in flexible packaging industry using genetic algorithm. IOP Conf. Series. Earth and Environmental Science, 472, 012050. doi:10.1088/1755-1315/472/.
Marimin, Darmawan, M.A., Widhiarti, R.P., & Teniwut, Y.K., 2018. Green productivity improvement and sustainability assessment of the motorcycle tire production process: A case study. J. Clean. Prod. 191, 273–282. https://doi.org/10.1016/j.jclepro.2018.04.228
Marqués,M. (2017). Sostenibilidad, comunicación y valor compartido: el discurso actual del desarrollo sostenible en la empresa española (Tesis Doctoral). Madrid: Universidad Complutense de Madrid.
Martínez,J. (2017). Aplicación de los instrumentos Brief y Best en la detención de riesgo ergonómico en la industria metalmecánica. Revista Tog, 14(26), 374-385
Martínez León, H.C., & Calvo-Amodio, J., 2017. Towards lean for sustainability: Understanding the interrelationships between lean and sustainability from a systems thinking perspective. J. Clean. Prod. 142, 4384–4402. https://doi.org/10.1016/j.jclepro.2016.11.132
Martínez,S., Pérez,E., Eguía, P.,Erkorekab,A., & Granada,E. (2020). Model calibration and exergoeconomic optimization with NSGA-II applied to a residential cogeneration. Applied Thermal Engineering, 169, 1-14. Recuperado de https://doi.org/10.1016/j.applthermaleng.2020.114916.
May, G., Stahl, B., Taisch, M., & Prabhu, V. (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071-7089. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/002.
May, G., Stahl, B., Taisch, M., & Kiritsis, D., 2017. Energy management in manufacturing: from literature review to a conceptual framework. J. Clean. Prod. 167, 1464e1489.
McCarthy. (1959). Programs With Common Sense. Computer Science Department. Stanford University, 1-15.
McCarthy, J. (1956). Dartmouth Summer Research Conference on Artificial Intelligence. Dartmouth College.
Meiting,L., & Hua,W. (2020). Angular or rounded? The effect of the shape of green brand logos on consumer perception. Journal of Cleaner Production, 279, 1-7.
Mekni, S., & Chaâr, B. (2014). An enhanced PSO-based algorithm for multiobjective flexible job shop scheduling problems(FJSSPs). Proceedings of the 6th International Conference on Management of Emergent Digital EcoSystems - MEDES, 14 (12), 178–182. Recuperado de http://doi.org/10.1145/2668260.2668289.
Melchio, C., & Henriqson, E. (2020). Systematic Literature Review on Sustainability Metrics. Rev. FSA, 17(2), 24–39. Recuperado de http://www4.fsanet.com.br/revista/index.php/fsa/article/view/1930.
Meng,L., Zhang,C. Shao,X., & Ren,Y. (2019). MILP models for energy-aware flexible job shop scheduling problem. Journal of Cleaner Production, 210, 710- 723. Recuperado de https://doi.org/10.1016/j.jclepro.2018.11.021.
Min Liu a, Xifan Yao a,∗, Yongxiang Li a,b. (2020). Hybrid whale optimization algorithm enhanced with Lévy flight and differential evolution for job shop scheduling problems. Applied Soft Computing Journal , 87, 1-16. Recuperado de https://doi.org/10.1016/j.asoc.2019.105954.
Min, H., & Choi, S.B. (2019). Green sourcing practices in Korea. Manag. Res. Rev. 43, 1–18. https://doi.org/10.1108/MRR-11-2018-0446
Dai,M., Dunbing,T., Giret,A.,& Salido,M. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer Integrated Manufacturing, 59 (2019) 143–157. Recuperado de https://doi.org/10.1016/j.rcim.2019.04.006.
Minambiente. (2019). Estudio Nacional del agua. Obtenido de http://documentacion.ideam.gov.co/openbiblio/bvirtual/023858/ENA_2018.pdf
Ministerio para la Transición Ecologica y el Reto Demográfico. (2020). Registro de huella de carbono, compensación y proyectos de absorción de dióxido de carbono. España.
Minsky,M. (1985). Communication with Alien Intelligence. Byte: Artificial Intelligence, 10(4), 127-138.
Minsky,M. (1961). Steps Toward Artificial Intelligence. Proceedings Of The Ire, 8-30.
Mohammadi,S., Al-e-Hashem,M., & Rekik,Y. (2020). An integrated production scheduling and delivery route planning with multi-purpose machines: A case study from a furniture manufacturing company. International Journal of Production Economics, 219, 347–359. Recuperado de https://doi.org/10.1016/j.ijpe.2019.05.017.
Mokhtari, H., & Dadgar, M. (2015). Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate. Computers & Operations Research, 61, 31–45. Recuperado de http://doi.org/10.1016/j.cor.2015.02.014.
Mokhtari,H & Hasani,A. (2017). An energy-efficient multi-objective optimization for flexible job-shopscheduling problem. Computers and Chemical Engineering, 104 (2017) 339–352. Recuperado de http://dx.doi.org/10.1016/j.compchemeng.2017.05.004.
Moniz,D., Pedro,J., Horta,N., & Pires,J. (2019). Multi-objective framework for cost-effective OTN switch placement using NSGA-II with embedded domain knowledge. Applied Soft Computing Journal, 83, 1-12. recuperado de https://doi.org/10.1016/j.asoc.2019.105608.
Montalban,L., García,L., Sanz,A., & Pellicer,E. (2018). Social sustainability criteria in public-work procurement: An international perspective. Journal of Cleaner Production , 198, 1355- 1371. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.083.
Moon, J., & Park, J. (2014). Smart production scheduling with time-dependent and machine-dependent electricity cost by considering distributed energy resources and energy storage. International Journal of Production Research, 52(13), 3922-3939. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/00207543.2013.860251.
Moore,L., Wurzelbacher,S., & Shockey,T. (2018). Workers' compensation insurer risk control systems: Opportunities for public health collaborations. Journal of Safety Research, 66, 141–150.Recuperado de https://doi.org/10.1016/j.jsr.2018.07.004.
Moradi,S., Fatemi,G., & Zandieh,M. (2011). Bi-objective optimization research on integrated fixed time interval preventive maintenance and production for scheduling flexible job-shop problem. Expert Systems with Applications, 38, 7169–7178.
Morais,D.,& Silvestre,D. (2018). Advancing social sustainability in supply chain management: Lessons from multiple case studies in an emerging economy. Journal of Cleaner Production, 199, 222-235. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.097.
Moslehi,G., & Mahnam,M. (2011). A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search. Int. J. Production Economics, 12(9), 14–22.
Mülling,D., Land,A., Seuring,S., & Machado, L. (2018). Linking sustainability-oriented innovation to supply chain relationship integration. Journal of Cleaner Production, 172, 3448-3458. Recuperado de https://doi.org/10.1016/j.jclepro.2017.11.091.
Murphy,Y., Brennan, E., & Flessner,E. (2019). Anxiogenic parenting practices as predictors of pediatric body-focused repetitive behaviors. Journal of Obsessive-Compulsive and Related Disorders , 21, 46–54. Recuperado de https://doi.org/10.1016/j.jocrd.2018.12.002.
Muth, J. F., & Thompson, G. L. (1963). Industrial Scheduling, Prentice Hall, Englewood Cliffs, New Jersey, Ch 15, 225-251.
Naciones Unidas. (2015). Convención Marco sobre el Cambio Climático. Paris.
Ning, T.,& Jin,H. (2018). A cloud based improved method for multi-objective flexible job-shop scheduling problem. Journal of Intelligent and Fuzzy Systems, 35 (12), 823-829.
Nitisiria,K., Gena,M., & Ohwadaa,M. (2019). A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling. Computers & Industrial Engineering, 130, 381–394. Recuperado de https://doi.org/10.1016/j.cie.2019.02.035.
Norma GT 45. (2015). Instituto Colombiano de Normas Técnicas y Certificación. Guía para la identificación de los peligros y la valoración de los riesgos en seguridad y salud ocupacional. Bogotá: Icontec.
Noticias ONU. (2018). El Acuerdo de París sobre cambio climático no es suficiente. Obtenido de https://news.un.org/es/interview/2018/09/1441622
Noticias ONU. (2019). Termina la COP25 con pocos avances en cuanto a la reducción de emisiones de carbono. Obtenido de https://news.un.org/es/story/2019/12/1466671
Noticias ONU. (2020). El cambio climático es más mortal que el coronavirus. Obtenido de https://news.un.org/es/story/2020/03/1470901
Nouiri, M., Bekrar,A., & Trentesaux.D. (2018). Towards Energy Efficient Scheduling and Rescheduling for Dynamic Flexible Job shop problem. IFAC (International Federation of Automatic Control) Papers On Line , 51(11), 1275–1280. doi: 10.1016/j.ifacol.2018.08.357.
Nouiri,M., Bekrar,A., Jemai,A., Niar,S., & Ammari,A. (2018a). An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem. J Intell Manuf, 29, 603–615. Recuperado de https://doi.org/10.1007/s10845-015-1039-3.
Nouri,H. (2016). Development of a comprehensive model and BFO algorithm for a dynamic cellular manufacturing system. AppliedMathematicalModelling, 40, 1514–1531. Recuperado de http://dx.doi.org/10.1016/j.apm.2015.09.004.
NTC ISO 14001. (2015). Sistemas de Gestión Ambiental. Requisitos con orientación para su uso. Bogotá: Icontec. Instituto Colombiano de Normas Técnicas y Certificación .
Ojstersek, R., Lalic, D., Buchmeister, B. (2019). A new method for mathematical and simulation modelling interactivity: A case study in flexible job shop scheduling. Advances in Production Engineering & Management, 14(4), 435–448.
Okoshia,C., Limab,E., & Gouvea,E . (2019). Performance cause and effect studies: Analyzing high performance manufacturing companies. International Journal of Production Economics, 210, 27–41. Recuperado de https://doi.org/10.1016/j.ijpe.2019.01.003.
Onyeocha, C. (2014). Robust Production & Inventory Control Systems for Multi-product Manufacturing Flow Lines. Universidad de Dublín.
Ouoba,Y. (2017). Economic sustainability of the gold mining industry in Burkina Faso. Resources Policy, 51, 194–203. Recuperado de http://dx.doi.org/10.1016/j.resourpol.2017.01.001.
Owais,M., & Osman,M. (2018). Complete hierarchical multi-objective genetic algorithm for transit network design problem. Expert Systems With Applications, 114, 143–154.
Ozcan,S., & Simsir,F. (2019). A new model based on Artificial Bee Colony algorithm for preventive maintenance with replacement scheduling in continuous production lines. Engineering Science and Technology, an International Journal , 22, 1175–1186.
Ozturk,G., Bahadir,O., & Teymourifar,A. (2018). Extracting priority rules for dynamic multiobjective flexible job shop scheduling problems using gene expression programming. International Journal of Production Research, 57(19), 3121–3137.
Pagell, M., & Shevchenko, A. (2014). Why research in sustainable supply chain management should have no future. Development of Truly Sustainable Supply Chains, 50(1), 44–55.
Paik,S., & Zalk,D. (2019). A Simple Proposition for Improving Industrial Hygiene Air Sampling Methods. Safety and Health at Work , 10, 389- 392. Recuperado de https://doi.org/10.1016/j.shaw.2019.07.001.
Pajares,G & Santos,M. (2006). Inteligencia artificial e inteligencia del conocimiento . México: Alfaomega.
Palacios, J. J., González, M. A., Vela, C. R., González, I., & Puente, J. (2015). Genetic tabu search for the fuzzy flexible job shop problem. Computers & Operations, 54, 74–89. http://doi.org/10.1016/j.cor.2014.08.023.
Pandian, P., Sankar,S., Ponnambalam & Raj,V. (2012). Scheduling of Automated Guided Vehicle and Flexible Jobshop using Jumping Genes Genetic Algorithm. American Journal of Applied Sciences, 9 (10), 1706-1720.
Pehrsson, L. (2013). Manufacturing Management And Decision support using simulation-based Multi-objective Optimisation. Suecia: Universidad de Skovde.
Pérez,R., & Hernández,A. (2018). A hybrid estimation of distribution algorithm for flexible job-shop scheduling problems with process plan flexibility. Applied Intelligence, 48:3707–3734. Recuperado de https://doi.org/10.1007/s10489-018-1160-z.
Pezzellaa,F., Morgantia, G., Ciaschettib,F. (2008). A genetic algorithm for the Flexible Job-shop Scheduling Problem. Computers & Operations Research, 35, 3202 – 3212. doi:10.1016/j.cor.2007.02.014.
Pinedo, M. (2005). Planning and scheduling in manufacturing and services. Vol. 24. New York: Springer.
Pinedo, M. (2016). Scheduling: Theory, Algorithms and Systems. Fifth Edition. New York: Springer.
Pires,J., Ferreira,A.,Bartocci,L., & Pinheiro,D. (2019). Systemic indicator of sustainable development: Proposal and application of a framework. Journal of Cleaner Production 241 (2019) 118383, 241,1-10. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118383.
Piroozfard, H., Wong,K., & Peng, W. (2018). Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 128, 267–283. Recuperado de http://dx.doi.org/10.1016/j.resconrec.2016.12.001.
PNUD. (2016). Objetivos de Desarrollo Sostenible. Herramientas de aproximación al contexto local. Obtenido de https://www.undp.org/content/dam/colombia/docs/ODM/undp-co-ODSColombiaVSWS-2016.pdf
Popovic a,T., Barbosa,A., Kraslawski,A.,& Carvalho,A. (2018). Quantitative indicators for social sustainability assessment of supply. Journal of Cleaner Production, 180, 748- 768. Recuperado de https://doi.org/10.1016/j.jclepro.2018.01.142.
Pradhan, M & Panda,G. (2017). Information Combining Schemes for Cooperative Spectrum Sensing: A Survey and Comparative Performance Analysis. Wireless Pers Commun, 94, 685–711. doi: 10.1007/s11277-016-3645-6.
Purshouse,R. (2003). On the Evolutionary Optimisation of Many Objectives. The University of Sheffield.
Quezada,W., Hernández,G., González,E., Rodríguez,R., & Molina,F. (2018). Gestión de la tecnología y su proceso de transferencia en Pequeñas y Medianas Empresas metalmecánicas del Ecuador. Ingeniería Industrial, 3(8), 303-314. Recuperado de http://www.rii.cujae.edu.cu.
Quizá, R. (2004). Optimización multiobjetivo del proceso de torneado. Cuba: Universidad de Matanzas .
Rabiee , M., Zandieh & Ramezani. (2012). Bi-objective partial flexible job shop scheduling problem: NSGA-II, NRGA, MOGA and PAES approaches. International Journal of Production Research, 50 (24), 7327–7342.
Rahmani, A., Siar, H., Shamshirband, S., Shojafar, M., & Nasir, M. (2015). Using the gravitational emulation local search algorithm to solve the multi-objective flexible dynamic job shop scheduling problem in Small and Medium Enterprises. Annals of Operat, 229(1), 451-474.
Rahmati, S., Zandieh, M., & Yazdani, M. (2012). Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 64(5-8), 915–932. Recuperado de http://doi.org/10.1007/s00170-012-4051-1.
Rahmati,S., Ahmadi,A.,& Karimi,B. (2018). Multi-objective evolutionary simulation based optimization mechanism for a novel stochastic reliability centered maintenance problem. Swarm and Evolutionary Computation, 40, 255–271. Recuperado de https://doi.org/10.1016/j.swevo.2018.02.010.
Ramachandrana,S.,Jayalal, M., Riyas,A., Jehadeesan, R & Devan,K. (2020). Application of genetic algorithm for optimization of control rods positioning in a fast breeder reactor core. Nuclear Engineering and Design , 361, 1-14. Recuperado de https://doi.org/10.1016/j.nucengdes.2020.110541.
Ramanathan, T., & Ting, Y. (2015). Selection of wet digestion methods for metal quantification in hazardous solid wastes. Journal of Environmental Chemical Engineering, 3(3), 1459–1467. Recuperado de http://doi.org/10.1016/j.jece.2015.05.006.
Ramicic,M., & Bonarini,A. (2020). Adaptation of learning agents through artificial perception. Adaptive Behavior, 28(2),79-89.
Reddy, S., Ratnam, Ch., Rajyalakshmi, G., & Manupati, V.K. (2018). An effective hybrid multi objective evolutionary algorithm for solving realtime event in flexible job shop scheduling problem. Measurement , 114 (20) 78–90. Recuperado de https://doi.org/10.1016/j.measurement.2017.09.022.
Rena,W., Wena,J., Hua,Y., & Lib,J. (2020). Maintenance service network redesign for geographically distributed moving assets using NSGA-II in agriculture. Computers and Electronics in Agriculture , 169. Recuperado de https://doi.org/10.1016/j.compag.2019.105170.
Renna, P. (2012). Controllable processing time policies for job shop manufacturing system. . The International Journal of Advanced Manufacturing Technology, 67(9-12), 2127–2136. http://doi.org/10.1007/s00170-012-4635-9.
Resoloución A/68/970. (2014). Informe del Grupo de Trabajo Abierto de la Asamblea General sobre los Objetivos de Desarrollo Sostenible. Ginebra: Naciones Unidas.
Resolución 2734 . (2010). “Por la cual se adoptan los requisitos y evidencias de contribución al desarrollo sostenible del país y se establece el procedimiento para la aprobación nacional de proyectos de reducción de emisiones de gases de efecto invernadero que optan al Mecanismo . Bogotá: Ministerio de Ambiente, Vivienda y Desarrollo Territorial .
Pontes,R., Ferreira,A., & Custodio,D. (2020). Application of Hybrid Simulation in production scheduling in job shop systems Modeling and Simulation International. Simulation: Transactions of the Society for, 96(3), 253-268.
Rohaninejad, M., Sahraeian, R., & Nouri, B. (2016). Multi-objective optimization of integrated lot-sizing and scheduling problem in flexible job shops. Operations Research, 50(3), 587-609. http://dx.doi.org/10.1051/ro/2015049.
Romagnoli,G. (2015). Design and simulation of CONWIP in the complex flexible job shop of a Make-To-Order manufacturing firm. International Journal of Industrial Engineering Computations, 6, 117–134, doi:10.5267/j.ijiec.2014.8.003.
Rong-Hwa., Tung-Han,Y. (2017). An effective ant colony optimization algorithm for multi-objectivejob shop scheduling with equal-size lot-splittingRong. Applied Soft Computing, 57, 642–656. Recuperado de http://dx.doi.org/10.1016/j.asoc.2017.04.062.
Rossi,A.,& Dini,G. (2007). Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method. Robotics and Computer-Integrated Manufacturing, 23, 503–516. doi:10.1016/j.rcim.2006.06.004.
Rosso,F., Ciancio,F.,Dell’Olmo,J., & Salata, F. (2020). Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application. Energy & Buildings , 216, 1-18. Recuperado de https://doi.org/10.1016/j.enbuild.2020.109945.
Ruan, J., & Xu, Z. (2016). Constructing environment-friendly return road of metals from e-waste: Combination of physical separation technologies. Renewable and Sustainable Energy Reviews, 54, 745–760. Recuperado de http://doi.org/10.1016/j.rser.2015.10.114.
Rubaiee, S., & Yildirim, M.B., 2019. An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Comput. Ind. Eng. 127, 240–252. https://doi.org/10.1016/j.cie.2018.12.020
Ruiz, M.R., Briceño, L.J., Severiche, C.A., & Durán, L. (2019). Legal framework of environmental management for agricultural SMEs: Caribbean Colombian Context. . Revista Espacios, 40(32), 9-15. Recuperado de https://www.revistaespacios.com/a19v40n32/a19v40n32p09.pdf.
Ruiz, S. (2015). Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica. Universidad Nacional de Colombia.
Russell, S., & Norvig, P. (2004). Inteligencia Artificial: un enfoque moderno. México: Printice Hall.
Saad,M., Nazzala,N., & Darrasa,B. (2019). A general framework for sustainability assessment of manufacturing processes. Ecological Indicators, 97, 211–224. Recuperado de https://doi.org/10.1016/j.ecolind.2018.09.062.
Sahu,A., Padhy,R., Das,D., & Gautam,A. (2020). Improving financial and environmental performance through MFCA: A SME case study. Journal of Cleaner Production, 123751, 1-21.
Sajan, M.P., Shalij, P.R., Ramesh, A., Biju Augustine, P., 2017. Lean manufacturing practices in Indian manufacturing SMEs and their effect on sustainability performance. J. Manuf. Technol. Manag. 28, 772e793. https://doi.org/10.1108/ JMTM-12-2016-0188.
Salas,K., Meza,J., Obredor,T., & Mercado,N. (2019). Evaluación de la Cadena de Suministro para Mejorar la Competitividad y Productividad en el Sector Metalmecánico en Barranquilla, Colombia. Información Tecnológica, 30 (2)., 25-32. Recuperado de http://dx.doi.org/10.4067/S0718-07642019000200025.
Salazar, E., & Sarzuri, R. (2015). Algoritmo genético mejorado para la minimización de la tardanza total en un Flowshop flexible con tiempos de preparación dependientes de la secuencia. Revista Chilena de Ingeniería, 23(1), 118-127.
Sangwa,N., & Sangwan,K. (2018). Development of an integrated performance measurement framework for lean organizations. Journal of Manufacturing Technology Management, 29(1).doi:10.1108/JMTM-06-2017-0098.
Sarache, W., Cárdenas, D., Giraldo J., & Parra J. (2007). Procedimiento para evaluar la estrategia de manufactura: aplicaciones en la industria metalmecánica. Pontificia Universidad Javeriana, Facultad de Ciencias Económicas y Administrativas.
Sarache, W., Cárdenas, D., Castrillón, O., Becerra, F., García, A., Giraldo, J., Ibarra, S., Ruiz, S., Tamayo, J., & Zapata, A. (2008). Gestión de la producción: Una aproximación conceptual. Unibiblios, Bogotá.
Sarangi,G., Mishrab, A., Changc,Y., & Taghizadeh,F. (2019). Indian electricity sector, energy security and sustainability: An empirical assessment. Energy Policy, 135, 1-15. Recuperado de https://doi.org/10.1016/j.enpol.2019.110964.
Scassellati,B., Brawer,J., Tsui,K., Gilani,s., Malzkuhn,M., Manini,B., & Stone,K . (2018). Teaching Language to Deaf Infants with a Robot and a Virtual Human. CHI, 21–26, doi: https://doi.org/10.1145/3173574.3174127.
Schaffer, J. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, Pittsburgh, PA, USA, 93-100. Recuperado de https://doi.org/10.1016/0066-4138(85)90462-8.
Segersted,E.,& Abrahamsson,L. (2019). Diversity of livelihoods and social sustainability in established mining communities. The Extractive Industries and Society , 6, 610–619. Recuperado de https://doi.org/10.1016/j.exis.2019.03.008.
Selander,J., Rylander,L.,Albin,M., Rosenhall,U.,Lewné,M., & Gustavsson. (2019). Full-time exposure to occupational noise during pregnancy was associated with reduced birth weight in a nationwide cohort study of Swedish women. Science of the Total Environmen, 1137–1143. https://doi.org/10.1016/j.scitotenv.2018.09.212.
SENA. (2012). Servicio Nacional de Aprendizaje. Caracterización del sector metalmecánico y área de soldadura. Bogotá.
SENA. (2013). Servicio Nacional de Aprendizaje. Sector metalmecánico: retos de cara al futuro. Obtenido de http://periodico.sena.edu.co/productividad/noticia.php?t=sector-metalmecanico-retos-de-cara-al-futuro&i=868
Sethi, P., Chakrabarti,D., & Hattacharjee,S. (2020). Globalization, financial development and economic growth: Perils on the environmental sustainability of an emerging economy. Journal of Policy Modeling, Recuperado de https://doi.org/10.1016/j.jpolmod.2020.01.007.
Shaheen,A., Spea, S., Farrag,S., & Abido,M. (2018). A review of meta-heuristic algorithms for reactive power planning problem. Ain Shams Engineering Journal , 9, 215–231. Recuperado de http://dx.doi.org/10.1016/j.asej.2015.12.003.
Shahsavari, P. N., & Ghasemishabankareh, B. (2013). A novel hybrid meta-heuristicalgorithm for solving multi objective flexible job shop scheduling. Journal of Manufacturing Systems, 32(4), 771–780.
Shao, X., Liu, W., Liu, Q., & Zhang, C. (2013). Hybrid discrete particle swarm optimization for multi-objective flexible job-shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 67(9-12), 2885–2901. Recuperado de http://doi.org/10.1007/s00170-012-4701-3.
Shen,X., & Yao, X. (2015). Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems. Information Sciences, 298, 198–224. recuperado de http://doi.org/10.1016/j.ins.2014.11.036.
Shen,L., Dauzère,S., Neufeld,D. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research , 265, 503–516. Recuperado de http://dx.doi.org/10.1016/j.ejor.2017.08.021.
Shen,X., Han,J., & Fu,J. (2017). Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft Comput, 21, 6531–6554. doi: 10.1007/s00500-016-2245-4.
Shi, J., Jiao, H., & Chen, T. (2012). Multi-objective pareto optimization on flexible job-shop scheduling problem about due punishment. Journal of Mechanical Engineering, 48(12), 184. Recuperado de https://scholar.google.es/scholar?q=Multi-objective+pareto+optimization+on+flexible+jobshop+scheduling+problem+about+due+punishment++&btnG=&hl=en&as_sdt=0%2C5#0.
Shiel,A., Paço,A., & Alves,H. (2020). Generativity, sustainable development and green consumer behaviour. Journal of Cleaner Production , 245,1-9. Recuperado de https://doi.org/10.1016/j.jclepro.2019.118865 .
Shivasankaran, N. (2015). Hybrid sorting immune simulated annealing algorithm for flexible job shop scheduling. International Journal of Computational Intelligence Systems, , 8(3), 455-466. Recuperado de http://www.tandfonline.com/doi/abs/10.1080/18756891.2015.1017383.
Shoham,Y. (1997). An Overview of Agent-Oriented Programming. En J. Bradshaw, Software Agents (págs. 271-290). Cambridge, Massachusetts: AAAI Press/The MIT Press.
Shokouhi,E. (2018). Integrated multi-objective process planning and flexible job shop scheduling considering precedence constraints. Production & Manufacturing Research, 6 (1), 61-89, Recuperado de https://doi.org/10.1080/21693277.2017.1415173.
Silva,P., Tavares,T., Botelhoa, Oliveira,P., Schaffert,P., Costa,R., & Rodrigues,J. (2020). Intrapopulation recurrent selection in sweet sorghum for improving sugar. Industrial Crops & Products, 143(2), Recuperado de https://doi.org/10.1016/j.indcrop.2019.111910.
Silva,R., Rab,L., Rodrigues,J.,Solic,P., & Carvalho,A. (2020). A preference-based demand response mechanism for energy management in a microgrid. Journal of Cleaner Production, 255 (11),1-14. Recuperado de https://doi.org/10.1016/j.jclepro.2020.120034.
Singh, A. (2013). Minimization of mean tardiness in a flexible job shop. International Journal of Simulation Modelling, 3, 190-204. Recuperado de http://ww.w.ijsimm.com/Full_Papers/Fulltext2013/text12-3_190-204.pdf.
Singh, M., Singh, M., Mahapatra, S., & Jagadev, N. (2016). Particle swarm optimization algorithm embedded with maximum deviation theory for solving multi-objective flexible job shop scheduling problem. International Journal of Advanced Manufacturing Technology, 85(9-12), 2353-2366. doi: 10.1007/s00170-015-8075-1.
Singh, A.P., & Dhadse, K., 2021. Economic evaluation of crop production in the Ganges region under climate change: A sustainable policy framework. J. Clean. Prod. 278, 123413. https://doi.org/10.1016/j.jclepro.2020.123413
Siqueira,C., SouzabS.R. & de Souza,C. (2018). A Multi-objective Variable Neighborhood Search algorithm for solving the Hybrid Flow Shop Problem. Electronic Notes in Discrete Mathematics, 66, 87-94. https://doi.org/10.1016/j.endm.2018.03.012.
Sitcharangsie,S., Ijomah,W., & Wong,T. (2019). Decision makings in key remanufacturing activities to optimise remanufacturing outcomes: A review. Journal of Cleaner Production, 232, 1465- 1481. Recuperado de https://doi.org/10.1016/j.jclepro.2019.05.204.
Srinivas,N & Kalyanmoy,D. (1995). Muiltiobj ective Optimization using nondominated sorting in Genetic Algorithms. Evolutionary Computation , 2(3), 22 1-248.
Staniskienhé, E., & Stankeviciuté, Z. (2018). Social sustainability measurement framework: The case of employee perspective in a CSR-committed organisation. Journal of Cleaner Production, 188, 708-719. Recuperado de https://doi.org/10.1016/j.jclepro.2018.03.269.
Steensels,J., Gallone,B.,Voordeckers,K.,& Verstrepen,K . (2020). Domestication of Industrial Microbes. Current Biology, https://doi.org/10.1016/j.cub.2019.04.025.
Steuer, R., Choo, E. (1983). An interactive weighted tchebychev procedure for multiple objective programming. Mathematical Programming, 26, 326-344. doi:10.1007/BF02591870C.
Suman,G., & Prajapati,D. (2018). Control chart applications in healthcare: a literature review. International Journal of Metrology and Quality Engineering, 9(5),1-21. Recuperado de https://doi.org/10.1051/ijmqe/2018003.
Sun,J., & Xu,L. (2019). Disruption management of multi-objective flexible job shop scheduling problem. Academic Journal Of Manufacturing Engineering, 11(2), 50-56.
Sun,X., Guo,S., & Du,B. (2019). A Hybrid Multi-Objective Evolutionary Algorithm With Heuristic Adjustment Strategies and Variable Neighbor-Hood Search for Flexible Job-Shop Scheduling Problem Considering Flexible Rest Time. IEEE Access, (7), 157003 - 157018.
Suthar,H., & Gadit,J. (2019). Multiobjective optimization of 2DOF controller using Evolutionary and Swarm intelligence enhanced with TOPSIS. Heliyon, 5(9), 2-28, doi: 10.1016/j.heliyon.2019.
Syafaruddin. (2019). Review on Multi-Objectives Optimization Methods in Hybrid Power Generation. Journal of Engineering Science and Technology Review 12 (1) (2019) 143- 152, 12(1)143 -152. doi:10.25103/jestr.121.17.
Ta, V., Griffith,C., Boatfield,C., Wang,X., Civitello,M., Bader, H & Loggarakis,A. (2020). User Experiences of Social Support From Companion Chatbots in Everyday Contexts: Thematic Analysis. Journal Of Medical Internet Research, 22,1-11. Recuperado de http://www.jmir.org/2020/2/e16235/.
Tadros,A., Sharon,M., Chill,N., Dragan,M., Rowell,J., & Hoffman,S. (2018). Emergency department visits for work-related injuries. American Journal of Emergency Medicine , 36, 1455–1458. Recuperado de https://doi.org/10.1016/j.ajem.2018.04.058.
Tamayo, J., Romero,E., Gamero,J., & Martínez,J. (2015). Do Innovation and Cooperation Influence SMEs' Competitiveness? Evidence From the Andalusian Metal-Mechanic Sector. Innovar, 25(55), 101-115. recuperado de http://dx.doi.org/10.15446/innovar.v25n55.47226.
Tamerabeta,Y., Adjadja,T ., & Bentrciaa. (2018). Evaluation of the genetic algorithm performance for the optimization of the grand potential in the cluster variation method. Calphad , 61, 157–164. Recuperado de https://doi.org/10.1016/j.calphad.2018.03.007.
Tanabe,K. (2018). Pareto’s 80/20 rule and the Gaussian distribution. Physica, 510, 635–640. Recuperado de https://doi.org/10.1016/j.physa.2018.07.023.
Tang, H., Chen, R., Li,Y., Z., Guo, P., & Du,Y. (2019). Flexible job-shop scheduling with tolerated time interval and limited starting time interval based on hybrid discrete PSO-SA: An application from a casting workshop. Applied Soft Computing Journal, 78, (21), 176–194. Recuperado https://doi.org/10.1016/j.asoc.2019.02.011.
Tay, J. C., & Ho, N. (2008). Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Computers & Industrial Engineering, 54(3), 453–473. recuperado de http://doi.org/10.1016/j.cie.2007.08.008.
Tian,N; & Ji,Z. (2015). Pareto-Ranking Based Quantum-Behaved Particle Swarm Optimization for Multiobjective Optimization. Hindawi Publishing Corporation, 7(9),1-11. Recuperado de http://dx.doi.org/10.1155/2015/940592.
Tian,S., Wang,T., Zhang,H., & 4, Wu,H. (2019). An Energy-Efficient Scheduling Approach for Flexible Job Shop Problem in an Internet of Manufacturing Things Environment. IEEEE Access. Special section on green communications on wireless networks, 8(11), 62695- 62704.
Trujillo, M. (2018). Indicador de desempeño ambiental bajo el enfoque GSCM: Validación en empresas manufactureras de la región del eje cafetero. Universidad Nacional de Colombia.
Vargas,Y. (2018). Perfil de salud laboral en Colombia a partir del análisis y caracterización de la enfermedad laboral reportada en el Sistema General de Riesgos Laborales. Periodo 2004 – 2014. Bogotá: Universidad Nacional de Colombia.
Velter,M., Bitzer,V., Bocken,N., & Kemp,R. (2020). Sustainable business model innovation: The role of boundary work for multi-stakeholder alignment. Journal of Cleaner Production , 247, 1-17. Recuperado de https://doi.org/10.1016/j.jclepro.2019.119497.
Venjakob,C., 1,2, Klein,A., Ebeling,A., Tscharntke,T., & Scherbe,C. (2016). Plant diversity increases spatio-temporal niche complementarity in plant-pollinator interactions. Ecology and Evolution, 6(8): 2249–2261.doi: 10.1002/ece3.2026.
Villeneuve,C., Tremblay,D., Riffon,O., Lanmafankpotin,G.,& Bouchard,S. (2017). A Systemic Tool and Process for Sustainability Assessment. Sustainability, 9-19. doi:10.3390/su9101909.
Vital,A., Azaba,A.,& Fazle Bakib,M. (2020). Mathematical modeling and a hybridized bacterial foraging optimization algorithm for the flexible job-shop scheduling problem with sequencing flexibility. Journal of Manufacturing Systems , 54, 74–93. Recuperado de https://doi.org/10.1016/j.jmsy.2019.11.010.
Wang, C., Ji, Z.,& Wang,Y. (2017). A Novel Memetic Algorithm Based on Decomposition for Multiobjective Flexible Job Shop Scheduling Problem. Mathematical Problems in Engineering, 1-21. Recuperado de https://doi.org/10.1155/2017/2857564.
Wang, G., Li, C., & Cui, H. (2013a). Simulation Optimization of Multi-Objective Flexible Job Shop Scheduling. Applied Mechanics and Materials, 365(5), 602-605. Recuperado de http://www.scientific.net/AMM.365-366.602.
Wang, S., Liu, C., Pei, D., & Wang, J. (2013b). A novel hybrid election campaign optimisation algorithm for multi-objective flexible job-shop scheduling problem. International Journal of Materials and Structural Integrity, 7(1/2/3), 160. Recuperado de http://doi.org/10.1504/IJMSI.2013.055113.
Wang, W., Fan, L., Xu, X., Zhao, Y., & Zhang, J. (2013c). Multi-objective differential evolution algorithm for flexible Job-Shop batch scheduling problem. Computer Integrated Manufacturing Systems, http://en.cnki.com.cn/Article_en/CJFDTOTAL-JSJJ201310013.htm19(10), 2481–2492. Recuperado de.
Wang, X., Gao, L., Zhang, C., & Shao, X. (2010a). A multi-objective genetic algorithm based on immune and entropy principle for flexible job-shop scheduling problem. International Journal of Advanced Manufacturing Technology, 51, 757-767. doi: 10.1007/s00170-010-2642-2. doi: 10.1007/s00170-013-4923-z.
Wang, X., Li, W., & Zhang, Y. (2013d). An improved multi–objective genetic algorithm for fuzzy flexible job–shop scheduling problem. International Journal of Computer, 47(2/3), 280-288.
Wang, Y., Feng, Y., Tan, J., Gao, Y. (2011). Multi-objective optimization method of flexible job-shop lot-splitting scheduling. Journal of Zhejiang University (Engineering Science), 45(4), 719-726.
Wang,J., Yang,J.,Zhang,Y., Ren,S & Liu,Y. (2020). Infinitely repeated game based real-time scheduling for low-carbon flexible job shop considering multi-time periods. Journal of Cleaner Production, 247, Recuperado de https://doi.org/10.1016/j.jclepro.2019.119093.
Wang,J., Zhang,B., Li, Lin., Bai, D., & Feng, Y. (2020a). Due-window assignment scheduling problems with position-dependent weights on a single machine, Engineering Optimization, 52:2, 185-193, DOI: 10.1080/0305215X.2019.1577411
Wang,L., Zhou, G., Xu,Y., & Min,L. (2012). An enhanced Pareto-based artificial bee colony algorithm for the multi-objective flexible job-shop scheduling. Int J Adv Manuf Technol , 11(60), 1111–1123.doi: 10.1007/s00170-011-3665-z.
Wang,Y.,& Shawb,D. (2018). The complexity of high-density neighbourhood development in China: Intensification, deregulation and social sustainability challenges. Sustainable Cities and Society, 43, 578–586. Recuperado de https://doi.org/10.1016/j.scs.2018.08.024.
Wassenhove,L.,& Gelders,L. (1980). Solving a bicriterion scheduling problem. European Journal of Operational Research, 4 (1), 42-48. Recuperado de https://doi.org/10.1016/0377-2217(80)90038-7Get rights and content.
Wolowski,L., Carvalheiro,L.,& Freitas,L. (2017). Influence of plant–pollinator interactions on the assembly of plant and hummingbird communities. Journal of Ecology 2017, 105, 332–344. doi: 10.1111/1365-2745.12684.
Woodcraft, S. (2012). Social sustainability and new communities: Moving from concept to practice in the UK. Procedia-Social and Behavioral Sciences, 68, 29–42. Recuperado de http://doi.org/10.1016/j.sbspro.2012.12.204.
Wouters,M.,& Stecher,J. (2017). Development of real-time product cost measurement: A case study in a medium-sized manufacturing company. Int. J. Production Economics, 183, 235–244. Recuperado de http://dx.doi.org/10.1016/j.ijpe.2016.10.018.
Wu,R., Li,Y., Guo,S., & Li,X. (2018). An Efficient Meta-Heuristic for Multi-Objective Flexible Job Shop Inverse Scheduling Problem. IEEE Access, 59515- 59527.
Wu,X.,& Sun,Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving. Journal of Cleaner Production, 3249- 3264. Recuperado de https://doi.org/10.1016/j.jclepro.2017.10.342.
Wu,W., Shen, X & Lib,C. (2019). The flexible job-shop scheduling problem considering deterioration effect and energy consumption simultaneously. Computers & Industrial Engineering , 135, 1004–1024. Recuperado de https://doi.org/10.1016/j.cie.2019.06.048.
Xia,W & Zu, Z. (2005). An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems. Computers & Industrial Engineering, 48, 409–425. doi:10.1016/j.cie.2005.01.018.
Xie,J., Gao,L., Peng,L.,Li,X., & Li,H. (2019). Review on flexible job shop scheduling. IET Collaborative Intelligent Manufacturing, 1(3), 67-77. doi: 10.1049/iet-cim.2018.0009.
Xing, L., Chen, Y., & Yang, K . (2008). Double Layer ACO Algorithm for the Multi-Objective FJSSP. New Generation Computing, 26(4), 313–327. Recuperado de http://doi.org/10.1007/s00354-008-0048-6.
Xing,L., Chen,Y., & Yang,K. (2009a). Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling. Applied Soft Computing, 9(11), 362–376.
Xing,L., Chen,Y.,& Yang,K. (2009b). An efficient search method for multi-objective flexible job shop. J Intell Manuf, 20, 283-293. doi: 10.1007/s10845-008-0216-z.
Xiong, J., Tan, X., Yang, K., Xing, L., & Chen, Y. (2012). A Hybrid Multiobjective Evolutionary Approach for Flexible Job-Shop Scheduling Problems. . Mathematical problems in engineering, 12(33), 12-27p. doi:10.1155/2012/478981.
Xiong, J., Xing, L., & Chen, Y. (2013). Robust scheduling for Multi-objective Flexible Job Shop problems with random machine breakdowns. International Journal of Production Economics, 141(1), 112–126. Recuperado de http://www.sciencedirect.com/science/article/pii/S0925527310001739.
Yang, Y., Huang, M.,Wang, Z.,Zhu, Q. (2018). Solving Bi-objective FJSP Using Limited Stable Matching Strategy. China Mechanical Engineering, 29(14), 1743-1750.
Yang,Y., Zeng,Z., Wang,R & Sun,X. (2016). Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times. journal One, 11(12), doi:10.1371/journal.pone.0167427.
Ye, J., & Ma, H. (2015). Multiobjective Joint Optimization of Production Scheduling and Maintenance Planning in the Flexible Job-Shop Problem. Mathematical Problems in Engineering, 4(11),1-9. Recuperado de http://www.hindawi.com/journals/mpe/2015/725460/abs/.
Yi,J., Xing,L., Wang, G.,Dong,J., Vasilakos,A., & Alavi, A. (2020). Behavior of crossover operators in NSGA-III for large-scale optimization problems. Information Sciences , 509, 470–487. Recuperado de https://doi.org/10.1016/j.ins.2018.10.005.
Yilmaz, O .,& Durmusolgu,C. (2018). A performance comparison and evaluation of metaheuristics for a batch scheduling problem in a multi-hybrid cell manufacturing system with skilled workforce assignment. Journal of Industrial and Management Optimization, 14(1), 1219-1249.
Yin,L., Li,X., Gao,L., Lu,C.,& Zhang,Z. (2017). A novel mathematical model and multi-objective method for the low-carbon flexible job shop scheduling problem. Sustainable Computing: Informatics and Systems, 13, 15–30. Recuperado de http://dx.doi.org/10.1016/j.suscom.2016.11.002.
Yu, W., & Shi,X. (2019). An intelligence evaluation method of the environmental impact for the cutting process. Journal of Cleaner Production , 227, 229- 236. Recuperado de https://doi.org/10.1016/j.jclepro.2019.03.336.
Yuan, K., Zhu, J., Ju,Q., & Wang, Y. . (2006). Integrated operator genetic algorithm for solving multi-objective flexible job-shop scheduling. Transactions of Nanjing University of Aeronautics and Astronautics, 278-282.
Yuan, Y., & Xu, H. (2015). Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms. Transactions on Automation Science and Engineering, 12(1), 336–353. Recuperado de http://doi.org/10.1109/TASE.2013.2274517.
Yuan,J., Chen,K., Li,W., Ji,C., Wang,Z., & Skibniewski,M. (2018). Social network analysis for social risks of construction projects in high-density urban areas in China. Journal of Cleaner Production, 198, 940- 961. Recuperado de https://doi.org/10.1016/j.jclepro.2018.07.109.
Zaman,F., Elsayed,S., Sarker,R., Essam, D., & Coello,C. (2021). An evolutionary approach for resource constrained project scheduling with uncertain changes. Computers and Operations Research 125 (2021) 105104, 125, 105104 - 105104.
Zapata Gomez, A. (2014). La gestión ambiental en el sector empresarial, una visión bajo el enfoque Empresa-Entorno como estrategia de competitividad. Bpgotá: Universidad Nacional de Colombia.
Zeleny, M. (1973). Compromise Programming. in Multiple Criteria Decision Making. Columbia, USA: University of South Carolina Press.
Zeng, Q., Yang,Y., Shen, L., Zhang, J. (2011). Multiobjective optimization for batch production FJSP based on just in time delivery. Computer Integrated Manufacturing Systems, 17(8),1780-1789.
Zhang, C.,Dong, X., Wang, X.,Li,X., & Liu,Q. (2010). Improved NSGA-II for the multi-objective flexible job-shop scheduling problem. Journal of Mechanical Engineering, 46(11), 156-16.
Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 56(4),1309–1318. Recuperado de http://doi.org/10.1016/j.cie.2008.07.0.
Zhang,H., Ge,H., Pan,R & Wu,Y. (2018a). Multi-Objective Bi-Level Programming for the Energy-Aware Integration of Flexible Job Shop Scheduling and Multi-Row Layout. Algorithms, 11, 210. doi:10.3390/a11120210.
Zhang,K., Shen,C., He,J., & Yen,G. (2020). Knee based multimodal multi-objective evolutionary algorithm for decision making. Information Sciences, 544, 1- 17.
Zhang,L., Tang,Q., Wu,Z., & Wang,F. (2017a). Mathematical modeling and evolutionary generation of rule sets for energy-efficient flexible job shops. Energy, 138, 210-227. Recuperado de http://dx.doi.org/10.1016/j.energy.2017.07.005.
Zhang,Y., Wang,J.,& Liu,Y. (2017b). Game theory based real-time multi-objective flexible job shop scheduling considering environmental impact. Journal of Cleaner Production, 16, 665-679. Recuperado de http://dx.doi.org/10.1016/j.jclepro.2017.08.068.
Zhang,Z., Wu,L., Peng,T., & Shun,J . (2018b). An Improved Scheduling Approach for Minimizing Total Energy Consumption and Makespan in a Flexible Job Shop Environment. Sustainability, 11(179), 1-21. doi:10.3390/su11010179.
Zheng, Y., Li, Y., & Lei, D. (2012). Multi-objective swarm-based neighborhood search for fuzzy flexible job shop scheduling. International Journal of Advanced Manufacturing Technology, 60 (9), 1063-1069. DOI: 10.1007/s00170-011-3646-2
Zhenwen,D. (2019). Application of multi-objective memetic algorithm in multi-objective flexible job-shop scheduling problem. Academic Journal of Manufacturing Engineering , 17(3), 24-28.
Zhou,B., & Liao,X. (2020). Particle filter and Levy flight-based decomposed multi-objective evolution hybridized particle swarm for flexible job shop greening scheduling with crane transportation. Applied Soft Computing Journal, 91, 1-18.
Zhou,X., & Peng,T. (2020). Application of multi-sensor fuzzy information fusion algorithm in industrial. Safety Science , 122,1-5. Recuperado de https://doi.org/10.1016/j.ssci.2019.104531.
Zhou,B., Liu,B., Yang, D., Cao,D., & Littler,T. (2020). Multi-objective optimal operation of coastal hydro-electrical energy system with seawater reverse osmosis desalination based on constrained NSGA-III. Energy Conversion and Management, 207, 1- 15. Recuperado de https://doi.org/10.1016/j.enconman.2020.112533.
Zhu, C., Qiu, W., Zhu, M., & Chen, G. (2016). Multi-objective flexible job shops robust scheduling problem under stochastic processing times. China Mechanical Engineering Magazine Office, 27, 1667-1672. doi: 10.3969/j.issn.1004-132X.2016.12.019.
Zhu, G., & Xu, W. (2019). Multi-objective flexible job shop scheduling method for machine tool component production line considering energy consumption and quality. Control and Decision, 34(2), 252-260.
Zhu,W. (2017). Multi-objective dynamic scheduling algorithm for flexible job-shop problem based on rule orientation. System Engineering Theory and Practice, 37(1), 2690-2699.
Zhu,Z., & Zhou,C. (2020). An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Computers & Industrial Engineering, 140, 1-16.
Zhu,H.,Hea,B., & Lib,H. (2017). Modified Bat Algorithm for the Multi-Objective Flexible Job Shop Scheduling Problem. International Lournal of Performability Endineering, 13 (7), 999-1012, doi: 10.23940/ijpe.17.07.p1.9991012.
Zitzler, E., Laumanns,M & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. ETH Zentrum. Research Collection, 1-22. Recuperado de https://doi.org/10.3929/ethz-a-004284029.
Zitzler,C., Laumanns,M., & Thiele,L. (2001). SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Computer Engineering and Networks Laboratory (TIK), 1-23.
Zitzler,E., & Thiele,L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. doi: doi: 10.1109/4235.797969.
Zitzler,E., Deb,K., & Thiele,L. (2000). Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8(2): 173-195, 8(2), 173-195.
Zou,S., Yaod, X., Zhongd,C., Zhaod, T.,& Huanga,H. (2019). Effectiveness of recurrent selection in Akebia trifoliata (Lardizabalaceae) breeding. Scientia Horticulturae 246 (2019) 79–85, 246, 79–85. Recuperado de https://doi.org/10.1016/j.scienta.2018.10.060. | |