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dc.contributor.author | Guzmán-Ortiz, Brunnel Eduardo | es_ES |
dc.contributor.author | Andres, B. | es_ES |
dc.contributor.author | Poler, R. | es_ES |
dc.date.accessioned | 2023-10-26T18:02:14Z | |
dc.date.available | 2023-10-26T18:02:14Z | |
dc.date.issued | 2022-05 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/198875 | |
dc.description.abstract | [EN] A wide variety of methods and techniques with multiple characteristics are used in solving replenishment, production and distribution planning problems. Selecting a solution method (either a solver or an algorithm) when attempting to solve an optimization problem involves considerable difficulty. Identifying the best solution method among the many available ones is a complex activity that depends partly on human experts or a random trial-and-error procedure. This paper addresses the challenge of recommending a solution method for replenishment, production and distribution planning problems by proposing a decision-making tool for algorithm selection based on the fuzzy TOPSIS approach. This approach considers a collection of the different most commonly used solution methods in the literature, including distinct types of algorithms and solvers. To evaluate a solution method, 13 criteria were defined that all address several important dimensions when solving a planning problem, such as the computational difficulty, scheduling knowledge, mathematical knowledge, algorithm knowledge, mathematical modeling software knowledge and expected computational performance of the solution methods. An illustrative example is provided to demonstrate how planners apply the approach to select a solution method. A sensitivity analysis is also performed to examine the effect of decision maker biases on criteria ratings and how it may affect the final selection. The outcome of the approach provides planners with an effective and systematic decision support tool to follow the process of selecting a solution method. | es_ES |
dc.description.sponsorship | The research leading to these results received funding from the European Union H2020 Programme with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)" and No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)" and from the Regional Department of Innovation, Universities, Science and Digital Society of the Generalitat Valenciana with Ref. PROMETEO/2021/065 "Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT). This work was supported by the Conselleria de Educación, Investigación, Cultura y Deporte¿Generalitat Valenciana for hiring predoctoral research staff with Grant (ACIF/2018/170) and the European Social Fund with the Grant Operational Programme of FSE 2014- 2020, the Valencian Community (Spain). Funding for open access charge: Universitat Politècnica de València. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Mathematics | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Fuzzy TOPSIS | es_ES |
dc.subject | Algorithm selection | es_ES |
dc.subject | Production planning | es_ES |
dc.subject | Heuristics | es_ES |
dc.subject | Metaheuristics | es_ES |
dc.subject | Matheuristics | es_ES |
dc.subject.classification | ORGANIZACION DE EMPRESAS | es_ES |
dc.title | A decision-making tool for algorithm selection based on a fuzzy TOPSIS approach to solve replenishment, production and distribution planning problems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/math10091544 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/825631/EU | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GENERALITAT VALENCIANA//ACIF%2F2018%2F170//AYUDA PREDOCTORAL GVA-GUZMAN/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/EC/H2020/958205/EU | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/CIUCSD//PROMETEO%2F2021%2F065//"Industrial Production and Logistics Optimization in Industry 4.0" (i4OPT) / | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Alcoy - Escola Politècnica Superior d'Alcoi | es_ES |
dc.description.bibliographicCitation | Guzmán-Ortiz, BE.; Andres, B.; Poler, R. (2022). A decision-making tool for algorithm selection based on a fuzzy TOPSIS approach to solve replenishment, production and distribution planning problems. Mathematics. 10(9):1-28. https://doi.org/10.3390/math10091544 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/math10091544 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 28 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 10 | es_ES |
dc.description.issue | 9 | es_ES |
dc.identifier.eissn | 2227-7390 | es_ES |
dc.relation.pasarela | S\464027 | es_ES |
dc.contributor.funder | European Commission | es_ES |
dc.contributor.funder | GENERALITAT VALENCIANA | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |
dc.contributor.funder | Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana | es_ES |
dc.subject.ods | 09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación | es_ES |