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Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach

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  • Gómez-Lagos, Javier E.
  • González-Araya, Marcela C.
  • Soto-Silva, Wladimir E.
  • Rivera-Moraga, Masly M.

Abstract

In a fruit harvest season, the fruit must be collected during a relatively short period of intense activity. Moreover, large fruit export companies commonly manage multiple orchards where the resources and labor are shared, making the decision process more complex. In this study, we address this harvest problem by proposing a mixed integer linear programming model for supporting tactical decisions during the harvest season in order to reduce total costs. This includes costs related to the fruit not reaching maturity and the number of harvest days. Due to the difficulty of solving this model optimally when real cases are considered, we developed a GRASP metaheuristic method. We compared the GRASP metaheuristic solution to the best integer solution obtained by an exact method using a real case. We observed that the metaheuristic produced a solution in less computational time than the best integer solution. The total costs obtained by the GRASP metaheuristic were two percent greater than the total cost obtained by the best integer solution. Additionally, we analyzed two scenarios to establish if the joint resource planning of the orchards would allow a cost reduction. The GRASP metaheuristic provides orchard managers with a harvest plan in a timely manner and adds greater flexibility to the decision process. The proposed model can be used to plan the harvesting of a variety of fresh fruits.

Suggested Citation

  • Gómez-Lagos, Javier E. & González-Araya, Marcela C. & Soto-Silva, Wladimir E. & Rivera-Moraga, Masly M., 2021. "Optimizing tactical harvest planning for multiple fruit orchards using a metaheuristic modeling approach," European Journal of Operational Research, Elsevier, vol. 290(1), pages 297-312.
    • Handle: RePEc:eee:ejores:v:290:y:2021:i:1:p:297-312
    DOI: 10.1016/j.ejor.2020.08.015
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    References listed on IDEAS

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