Managing quality, supplier selection, and cold‐storage contracts in agrifood supply chain through stochastic optimization
| Autor: | Wladimir E. Soto-Silva, Lluís M. Plà-Aragonés, Francesc Solsona-Tehas, Marcela C. González-Araya, Jordi Mateo-Fornés |
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| Rok vydání: | 2021 |
| Předmět: |
Dried apple
Operations research Stochastic modelling business.industry Computer science Strategy and Management media_common.quotation_subject Supply chain Perfect information Tactical planning Cold storage Management Science and Operations Research Two-stage mixed 0-1 Computer Science Applications Product (business) Renting Management of Technology and Innovation Agro-industry Quality (business) Stochastic optimization Business and International Management business media_common |
| Zdroj: | Repositorio Abierto de la UdL Universitad de Lleida |
| ISSN: | 1475-3995 0969-6016 |
| DOI: | 10.1111/itor.13069 |
| Popis: | The quality of such processed agrifood products as dehydrated apple is related to the quality and variety offresh harvested products and connected with wastage reduction throughout the agrifood supply chain. Forthis purpose, cold-storage management is important to avoid or mitigate the quality decay of fresh prod-ucts stored in refrigerated systems. This paper explores the benefits of a two-stage stochastic programmingmodel for product quality through the selection of producers and the management of cold storage to miti-gate deterioration and guarantee the maintenance of quality. A case study with real data from an agribusinesscompany is presented in the case study to illustrate and assess the suitability of the stochastic approach. Un-certainty regarding the conversion rate of fresh apples into the final dehydrated product and the purchasecost of the apples in the system are represented through scenarios generated from historical data. Recourseactions include the purchase of additional fruit and renting of additional cold stores to meet the demand.Based on the different scenarios, the value of the stochastic solution shows that modeling and solving theproposed stochastic model minimizes costs by an average of around 6.4%. In addition, the expected valueof perfect information demonstrates that using a proactive strategy could reduce costs by up to 9%. Theseresults ensure the applicability of this model in practice before and during the harvesting season for planningand replanning as uncertainty is revealed under a rolling horizon. The authors wish to acknowledge Marcos Oliva-Fernández for his valuable comments and sugges-tions and CYTED program for supporting the thematic network BigDSS-Agro (P515RT0123).D.Sc. Marcela González-Araya would like thank to FONDECYT Project 1191764 (Chile) forits financial support and D.Sc. Wladimir Soto-Silva would like to thank MEC Project 80180022(Chile). Moreover, this work was supported by the Ministerio de Economía y Competitividad un-der contract TIN2017-84553-C2-2-R and the Ministerio de Ciencia e Innovación under contractPID2020-113614RB-C22. Jordi Mateo and Francesc Solsona are members of the research group2017-SGR363 and Lluís M. Plà of 2017-SGR01193, funded by the Generalitat de Catalunya.Also, this research was partly supported by the European Union FEDER (CAPAP-H5 networkTIN2014-53522-REDT). |
| Databáze: | OpenAIRE |
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