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dc.contributor.authorGómez-Lagos, Javier E.
dc.contributor.authorGonzález-Araya, Marcela C.
dc.contributor.authorAcosta-Espejo, Luis G.
dc.contributor.authorSoto-Silva, Wladimir E.
dc.date.accessioned2024-05-07T16:04:46Z
dc.date.available2024-05-07T16:04:46Z
dc.date.issued2024
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5382
dc.description.abstractAn important stage of the agri-food supply chain is production (agricultural practices and harvesting), where relevant decisions are made in a short period of intensive activity. Regarding the major fruit supply chain, the complexity of these decisions increases because most fruit export companies manage several orchards with different fruit species and varieties. Moreover, the amount of labor and resources must be estimated at least two months in advance of the harvest so that they can be relied upon during the harvest season. In this study, a multi-objective modeling approach for supporting tactical harvest planning decisions of major fruits is developed, aiming to evidence its adequacy and to identify different harvest plans according to the prioritized objectives. Therefore, a multi-objective linear programming (MOLP) model, which considers three conflicting objectives, is proposed. The three objectives correspond to minimizing harvest costs, fruit loss and harvest days. For solving the MOLP model, five a posteriori multi-objective exact methods are compared using a synthetic instance based on a real case. The Pareto frontiers obtained by these methods are analyzed using three metrics related to accuracy, cardinality, and diversity criteria, so that the most suitable method can be selected. Thus, the selected method is applied to an actual case study of a Chilean fruit export company. In addition, the ε-constraint method is used in this case study to compare the obtention of non-dominated solutions in the convex and non-convex regions of the Pareto frontier. The results show that harvest plans vary significantly according to the prioritization of the objective functions. Thus, the percentage difference between each objective function’s worst and best values is around 497% when the harvest costs are minimized, around 1,448% when the fruit loss is minimized, and around 18% when the number of harvest days is minimized. In addition, the adequacy of the multi-objective approach rather than the mono-objective approach to support tactical harvest planning decisions is demonstrated. Furthermore, a scenario analysis for considering changes in the harvest periods is carried out by considering three possible scenarios. This analysis is required because these periods can change every year due to different factors, such as weather conditions and agricultural practices, among others. The results show that the harvest costs and fruit loss increase, on average, 87% and 100%, respectively, when the harvest period is short, that is, one day. Finally, managerial insights about the information obtained by the MOLP model are discussed.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceExpert Systems with Applications, 251, 123929es_CL
dc.subjectOR in Agriculturees_CL
dc.subjectMOLP modeles_CL
dc.subjectMOLP exact methodses_CL
dc.subjectMajor Fruit Tactical Harvest Planninges_CL
dc.subjectMOLP performance metricses_CL
dc.titleSupporting tactical harvest planning decisions of major fruits through a multi-objective modeling approach by using exact methodses_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.urisciencedirect.ucm.elogim.com/science/article/pii/S0957417424007954?via%3Dihubes_CL
dc.ucm.doidoi.org/10.1016/j.eswa.2024.123929es_CL


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