Comparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation

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dc.catalogadorvzp
dc.contributor.authorAvanzini, Elbio Leonel
dc.contributor.authorMac Cawley A.F.
dc.contributor.authorVera, Jorge R.
dc.contributor.authorMaturana Valderrama, Sergio
dc.date.accessioned2024-05-31T17:18:53Z
dc.date.available2024-05-31T17:18:53Z
dc.date.issued2021
dc.description.abstract© 2021 The Authors. International Transactions in Operational Research © 2021 International Federation of Operational Research SocietiesOperations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.
dc.description.funderANID
dc.description.funderFONDECYT
dc.fuente.origenScopus
dc.identifier.doi10.1111/itor.12982
dc.identifier.eissn14753995
dc.identifier.issn14753995 09696016
dc.identifier.scopusidSCOPUS_ID:85104753486
dc.identifier.urihttp://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1475-3995
dc.identifier.urihttps://doi.org/10.1111/itor.12982
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/86354
dc.identifier.wosidWOS:000642814300001
dc.information.autorucEscuela de Ingeniería; Avanzini Elbio Leonel; S/I; 1020042
dc.information.autorucEscuela de Ingeniería; Maturana Valderrama Sergio; 0000-0002-9998-7225; 61254
dc.language.isoen
dc.publisherBlackwell Publishing Ltd
dc.revistaInternational Transactions in Operational Research
dc.rightsacceso restringido
dc.subjectExpected value optimization
dc.subjectHarvest planning
dc.subjectOptimization
dc.subjectOR in agriculture
dc.subjectQuality management
dc.subjectStochastic programming
dc.subjectUncertainty modeling
dc.subject.ddc620
dc.subject.deweyIngeniería
dc.subject.ods12 Responsible consuption and production
dc.subject.odspa12 Producción y consumo responsable
dc.titleComparing an expected value with a multistage stochastic optimization approach for the case of wine grape harvesting operations with quality degradation
dc.typeartículo
sipa.codpersvinculados1020042
sipa.codpersvinculados61254
sipa.trazabilidadScopus;12-10-2021
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