dc.contributor | Jadán Avilés, Diana Carolina | |
dc.creator | Berrezueta Guamán, Nelson Bladimiro | |
dc.date.accessioned | 2020-02-28T12:35:02Z | |
dc.date.accessioned | 2022-10-20T21:25:36Z | |
dc.date.available | 2020-02-28T12:35:02Z | |
dc.date.available | 2022-10-20T21:25:36Z | |
dc.date.created | 2020-02-28T12:35:02Z | |
dc.date.issued | 2020-02-28 | |
dc.identifier | http://dspace.ucuenca.edu.ec/handle/123456789/34050 | |
dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/4603254 | |
dc.description.abstract | This research proposes k-NSGA-II, an algorithm based on evolutionary computing that mixes properties of micro-algorithms and artificial intelligence fundamentals such as clustering. The objective of k-NSGA-II is to optimize processes in real environments and to efficiently support decision making in organizations.
The changes developed were made in NSGA-II, a multiobjective genetic algorithm based on the non-dominance of its results. The functionality of k-NSGA-II was verified by performance tests comparing it with NSGA-II and µ-NSGA-II. These tests were performed on different objective functions and on a case study, which was based on the optimization of product production and distribution. The objectives were to minimize waste and maximize profit through sales.
k-NSGA-II was used to optimize the functions generating interesting results for the company. It was also more useful with respect to NSGA-II as it generated a small number of accurate solutions that the analyst could review quickly before making a decision, compared to NSGA-II which works with sets of 200 solutions or µ-NSGA-II which did not present solutions that could be useful to the company.
The k-NSGA-II algorithm presents an innovation with respect to NSGA-II as it is a precise micro algorithm whose solutions are very useful for decision making in real problem environments. It improves the evaluation time and avoids the analyst's fatigue because it does not present a great amount of results that many times are not analysed. | |
dc.language | spa | |
dc.publisher | Universidad de Cuenca | |
dc.relation | TN;793 | |
dc.subject | Ingeniería Industrial | |
dc.subject | Inteligencia artificial | |
dc.subject | Ingeniería de producción | |
dc.subject | Técnica de producción | |
dc.title | Optimización de la cadena de suministro mediante el uso de un algoritmo genético basado en clusterización | |
dc.type | bachelorThesis | |