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Web-Based Integer Programming Decision Support System for Walnut Processing Planning: The MeliFen Case

Author

Listed:
  • Carlos F. Brunner-Parra

    (Department of Transport Engineering and Logistics, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile)

  • Luis A. Croquevielle-Rendic

    (Industrial and Systems Engineering Department, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile)

  • Carlos A. Monardes-Concha

    (Industrial and Systems Engineering Department, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile
    School of Engineering, Universidad Católica del Norte, Coquimbo 1781421, Chile)

  • Bryan A. Urra-Calfuñir

    (School of Engineering, Universidad Católica del Norte, Coquimbo 1781421, Chile)

  • Elbio L. Avanzini

    (Industrial and Systems Engineering Department, Pontificia Universidad Católica de Chile, Santiago 7820436, Chile)

  • Tomás Correa-Vial

    (General Management Department, MeliFen, Paine 9540000, Chile)

Abstract

Chile is among the largest walnut producers and exporters globally, thanks to a favorable nut growth and production environment. Despite an increasingly competitive market, the literature offers little scientific advice regarding decision support systems (DSSs) for the nut sector. In particular, the literature does not present optimization approaches to support decision-making in walnut supply chain management, especially the processing planning. This work provides a DSS that allows the exporter to plan walnut processing decisions taking into account the quality of the raw material, such as size, color, variety, and external and internal defects, in order to maximize the benefits of the business. To formalize the problem, an integer programming model is proposed. The DSS was implemented via a web application for MeliFen, a walnut exporter located near Santiago, Chile. A comparative analysis of the last two years revealed that MeliFen increased its profit by approximately 9.8% using this tool. We also suggest other uses that this DSS provides, besides profit maximization.

Suggested Citation

  • Carlos F. Brunner-Parra & Luis A. Croquevielle-Rendic & Carlos A. Monardes-Concha & Bryan A. Urra-Calfuñir & Elbio L. Avanzini & Tomás Correa-Vial, 2022. "Web-Based Integer Programming Decision Support System for Walnut Processing Planning: The MeliFen Case," Agriculture, MDPI, vol. 12(3), pages 1-22, March.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:430-:d:775097
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    References listed on IDEAS

    as
    1. Ahumada, Omar & Rene Villalobos, J. & Nicholas Mason, A., 2012. "Tactical planning of the production and distribution of fresh agricultural products under uncertainty," Agricultural Systems, Elsevier, vol. 112(C), pages 17-26.
    2. Shabir Ahmad Mir & Theagarajan Padma, 2017. "Fuzzy decision support system for evaluation and prioritisation of critical success factors for the development of agricultural DSS," International Journal of Multicriteria Decision Making, Inderscience Enterprises Ltd, vol. 7(2), pages 146-172.
    3. Rose, David C. & Sutherland, William J. & Parker, Caroline & Lobley, Matt & Winter, Michael & Morris, Carol & Twining, Susan & Ffoulkes, Charles & Amano, Tatsuya & Dicks, Lynn V., 2016. "Decision support tools for agriculture: Towards effective design and delivery," Agricultural Systems, Elsevier, vol. 149(C), pages 165-174.
    4. Jakku, E. & Thorburn, P.J., 2010. "A conceptual framework for guiding the participatory development of agricultural decision support systems," Agricultural Systems, Elsevier, vol. 103(9), pages 675-682, November.
    5. Yang, Gaiqiang & Liu, Lei & Guo, Ping & Li, Mo, 2017. "A flexible decision support system for irrigation scheduling in an irrigation district in China," Agricultural Water Management, Elsevier, vol. 179(C), pages 378-389.
    6. Lundström, Christina & Lindblom, Jessica, 2018. "Considering farmers' situated knowledge of using agricultural decision support systems (AgriDSS) to Foster farming practices: The case of CropSAT," Agricultural Systems, Elsevier, vol. 159(C), pages 9-20.
    7. McCown, R. L., 2002. "Locating agricultural decision support systems in the troubled past and socio-technical complexity of `models for management'," Agricultural Systems, Elsevier, vol. 74(1), pages 11-25, October.
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