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A mathematical model for the optimization of agricultural supply chain under uncertain environmental and financial conditions: the case study of fresh date fruit

Author

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  • Mehran Gharye Mirzaei

    (K. N. Toosi University of Technology)

  • Saiedeh Gholami

    (K. N. Toosi University of Technology)

  • Donya Rahmani

    (K. N. Toosi University of Technology)

Abstract

In recent years, due to the rapid growth of the world’s population, the demand for agricultural products and food is growing increasingly. Therefore, the agricultural supply chain optimization has been grabbed by researchers to reduce food security concerns. On the other hand, the production amount of farmers is affected by various factors, including environmental conditions. In this paper, a supply chain network is investigated by developing a Mixed-Integer Linear Programming (MILP) model to effectively improve economic objectives under uncertainty. Then, a scenario-based robust optimization approach is employed to deal with the uncertainty. One of the novelities of our paper is considering weather conditions and economic fluctuations in different scenarios. The effectiveness of the proposed mathematical model has been confirmed by a real case study of dates farms. Dates and its by-products have a significant role in GDP, job creation, export, and the creation of various packaging and processing. Moreover, three meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and a hybrid algorithm based on them (WOA–PSO) are adapted to deal with the NP-hardness of the problems. Moreover, the parameters of the proposed algorithms are improved by the Taguchi method, and to achieve more exact measurements, sensitivity analysis is performed. Finally, the numerical results confirmed that the accuracy of the hybrid algorithm was between 1.9 and 2.8%. Therefore, this approach could be practical and efficient for solving large-sized problems. The obtained outcomes demonstrated that the planned model provides tactical considerations for the related managers.

Suggested Citation

  • Mehran Gharye Mirzaei & Saiedeh Gholami & Donya Rahmani, 2024. "A mathematical model for the optimization of agricultural supply chain under uncertain environmental and financial conditions: the case study of fresh date fruit," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(8), pages 20807-20840, August.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:8:d:10.1007_s10668-023-03503-7
    DOI: 10.1007/s10668-023-03503-7
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    References listed on IDEAS

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    1. Anderson, Edward & Monjardino, Marta, 2019. "Contract design in agriculture supply chains with random yield," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1072-1082.
    2. Mogale, D.G. & Kumar, Mukesh & Kumar, Sri Krishna & Tiwari, Manoj Kumar, 2018. "Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 111(C), pages 40-69.
    3. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.
    4. H. Grillo & M.M.E. Alemany & A. Ortiz & B. De Baets, 2019. "Possibilistic compositions and state functions: application to the order promising process for perishables," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 7006-7031, November.
    5. Azenith B. Castillo & Dan Jerry D. Cortes & Caesar F. Sorino & Christian Kim P. Soriño & Muftah H. El-Naas & Talaat Ahmed, 2023. "Bioethanol Production from Waste and Nonsalable Date Palm ( Phoenix dactylifera L.) Fruits: Potentials and Challenges," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    6. M. Boronoos & M. Mousazadeh & S. Ali Torabi, 2021. "A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3368-3395, March.
    7. van Berlo, Jules M., 1993. "A decision support tool for the vegetable processing industry; An integrative approach of market, industry and agriculture," Agricultural Systems, Elsevier, vol. 43(1), pages 91-109.
    8. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    9. Ge, Houtian & Nolan, James & Gray, Richard & Goetz, Stephan & Han, Yicheol, 2016. "Supply chain complexity and risk mitigation – A hybrid optimization–simulation model," International Journal of Production Economics, Elsevier, vol. 179(C), pages 228-238.
    10. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
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