dc.date.accessioned05/06/2019 9:19
dc.date.accessioned2022-09-23T14:44:17Z
dc.date.available05/06/2019 9:19
dc.date.available2022-09-23T14:44:17Z
dc.date.created05/06/2019 9:19
dc.date.issued2016-03-24
dc.identifier1860-949X
dc.identifierhttps://link.springer.com/chapter/10.1007/978-3-319-30337-6_11
dc.identifierhttp://hdl.handle.net/10818/35467
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/3483209
dc.description.abstractProduction scheduling in manufacturing systems is a highly complex task. It involves the allocation of limited resources (machines, tools, personnel, etc.) for the execution of specific jobs. In the case of workforce scheduling, unique human characteristics must also be considered, further complicating the task, as similar characteristics are not present in machines. Numerous published research works have examined issues of workforce scheduling by evaluating employee characteristics. Moreover, as business policies must nowadays sup-port social responsibility objectives, academic works have considered this dimension. This paper reviews academic literature on workforce scheduling strategies that consider social responsibility issues in order to identify quantitative methods and techniques employed. A systematic literature review is conducted to form an objective, rigorous, and reproducible framework that minimizes biases in the inclusion/exclusion of analysed works. Applications, trends, and gaps are identified, thus identifying pertinent avenues for future research.
dc.languageeng
dc.publisherStudies in Computational Intelligence
dc.relationService Orientation in Holonic and Multi-Agent Manufacturing, volume 640 pp 115-123
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsopenAccess
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.sourceUniversidad de La Sabana
dc.sourceIntellectum Repositorio Universidad de La Sabana
dc.titleSustainability in Production Systems: A Review of Optimization Methods Studying Social Responsibility Issues in Workforce Scheduling
dc.typejournal article


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