dc.creator | Corsini, Roberto R. | es |
dc.creator | Costa, Antonio | es |
dc.creator | Fichera, Sergio | es |
dc.creator | Framiñán Torres, José Manuel | es |
dc.date.accessioned | 2023-02-01T13:42:12Z | |
dc.date.available | 2023-02-01T13:42:12Z | |
dc.date.issued | 2022 | |
dc.identifier.citation | Corsini, R.R., Costa, A., Fichera, S. y Framiñán Torres, J.M. (2022). A new data-driven framework to select the optimal replenishment strategy in complex supply chains. En 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, IFAC PapersOnLine 55, 10 (1423-1428). DOI:10.1016/j.ifacol.2022.09.590 | |
dc.identifier.issn | 2405-8963 | es |
dc.identifier.uri | https://hdl.handle.net/11441/142303 | |
dc.description | - Part of special issue: 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022: Nantes, France, 22-24 June 2022.
Edited by Alain Bernard, Alexandre Dolgui, Hichem Haddou Benderbal, Dmitry Ivanov, David Lemoine, Fabio Sgarbossa
- Copyright © 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) | es |
dc.description.abstract | Motivated by the high variability of markets occurred in the last years, which in turns determined significant uncertainty in lead times and supply chain dynamics, this paper introduces a data-driven framework based on machine learning and metaheuristic optimization to dynamically select the most suitable replenishment strategy for a complex two-echelon (supplier-inventory-factory) supply chain (SC) problem with perishable product and stochastic lead times. Since the supplier dispatches the product (i.e., the raw material) with a fixed expiration date, the product shelf-life strictly depends on the related delivery lead time, which is subject to uncertainty. In addition, a minimum order quantity has to be fulfilled and the time between two consecutive orders cannot be less than one month. The aim of the work is to select the most suitable replenishment strategy able to minimize the average stock level, which is a surrogate cost metric, while respecting a target fill rate. Considering a smoothing order-up-to policy, the data-driven prediction-optimization framework makes use of Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) to select the best replenishment parameters (i.e., forecasting factor, proportional controller and safety stock factor) able to dynamically enhance the SC economic performance under the fill rate constraint. The ability of the framework under the predictive and the optimization perspective is assessed and a sensitivity analysis on the influence of replenishment parameters is presented as well. | es |
dc.format | application/pdf | es |
dc.format.extent | 6 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, IFAC PapersOnLine 55, 10 (2022), pp. 1423-1428. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Supply chain dynamics | es |
dc.subject | Perishable product | es |
dc.subject | Disruption | es |
dc.subject | Machine learning | es |
dc.subject | Metaheuristic algorithm | es |
dc.subject | Cyber-physical system | es |
dc.subject | Simulation optimization | es |
dc.title | A new data-driven framework to select the optimal replenishment strategy in complex supply chains | es |
dc.type | info:eu-repo/semantics/conferenceObject | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S2405896322018997 | es |
dc.identifier.doi | 10.1016/j.ifacol.2022.09.590 | es |
dc.contributor.group | Universidad de Sevilla. TEP134: Organización Industrial | es |
dc.publication.initialPage | 1423 | es |
dc.publication.endPage | 1428 | es |
dc.eventtitle | 10th IFAC Conference on Manufacturing Modelling, Management and Control, MIM 2022, IFAC PapersOnLine 55, 10 | es |
dc.eventinstitution | Nantes | es |