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<p>Reevaluating the food supply chain network is crucial in today’s fast-changing world. This paper dives into the process of redesigning the food supply chain network. It considers the impact of pandemic disruptions and multiple aspects of uncertainty, including random and deep ones, while highlighting the importance of resilience and responsiveness. The discussion covers various aspects of this redesign effort, including facility location decisions, inventory management, and distribution strategies. A hybrid robust stochastic optimization approach is used to handle the problem uncertainty. Furthermore, the paper investigates the integration of resilience strategies, including fortification, capacity expansion, and dual-channel distribution, to enhance the network's ability to withstand disruptions. Also, the Benders Decomposition algorithm is employed to address large-scale instances. A set of test instances are generated to assess the model's performance. Across 15 test problems, the proposed method consistently exhibited a smaller relative error, substantially improving 22 percent compared to the nominal approach. These findings highlight the model’s potential as a valuable tool for designing resilient and responsive supply chains to aid decision-making.</p>

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Robust, resilient, and responsive food supply chain redesign considering uncertainty and pandemic disruptions

  • 期刊论文

  • 发表日期：

    2025年1月23日

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  • Hanieh Shekarabi,
  • Ashkan Mozdgir

摘要

Reevaluating the food supply chain network is crucial in today’s fast-changing world. This paper dives into the process of redesigning the food supply chain network. It considers the impact of pandemic disruptions and multiple aspects of uncertainty, including random and deep ones, while highlighting the importance of resilience and responsiveness. The discussion covers various aspects of this redesign effort, including facility location decisions, inventory management, and distribution strategies. A hybrid robust stochastic optimization approach is used to handle the problem uncertainty. Furthermore, the paper investigates the integration of resilience strategies, including fortification, capacity expansion, and dual-channel distribution, to enhance the network's ability to withstand disruptions. Also, the Benders Decomposition algorithm is employed to address large-scale instances. A set of test instances are generated to assess the model's performance. Across 15 test problems, the proposed method consistently exhibited a smaller relative error, substantially improving 22 percent compared to the nominal approach. These findings highlight the model’s potential as a valuable tool for designing resilient and responsive supply chains to aid decision-making.