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A Two-Stage Robust Optimization for Reliable Logistics Network Design via Evolutionary Computation

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  • Junqi He

    (Northeastern University, China)

  • Dongsheng Yang

    (Northeastern University, China)

  • Xin Wang

    (Northeastern University, China)

Abstract

This paper presents a novel two-stage robust optimization model for designing a dependable logistics network that integrates evolutionary computation techniques. The proposed model considers both the normal and disrupted states of the logistics network and seeks to reduce the overall network cost and operating time in different disruption situations. The challenge is a multi-objective optimization problem addressed using a hybrid evolutionary method that combines the advantages of the non-dominated sorting genetic algorithm with the large neighborhood search heuristic. Numerical experiments are conducted on various test instances to demonstrate the effectiveness and efficiency of the proposed model and algorithm. The results show that the proposed algorithm can generate robust and reliable logistics network designs resilient to disruptions and uncertainties, leading to significant improvements in logistics performance and cost savings compared to traditional methods.

Suggested Citation

  • Junqi He & Dongsheng Yang & Xin Wang, 2024. "A Two-Stage Robust Optimization for Reliable Logistics Network Design via Evolutionary Computation," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 15(1), pages 1-26, January.
    • Handle: RePEc:igg:jsir00:v:15:y:2024:i:1:p:1-26
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    References listed on IDEAS

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    1. Govindan, Kannan & Mina, Hassan & Alavi, Behrouz, 2020. "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    2. Dmitry Ivanov, 2022. "Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic," Annals of Operations Research, Springer, vol. 319(1), pages 1411-1431, December.
    3. Jia-Jiang Lin & Feng Xu & Xiong-Lin Luo, 2023. "Nonconvex sensitivity-based generalized Benders decomposition," Journal of Global Optimization, Springer, vol. 86(1), pages 37-60, May.
    4. Pierrick Pelé & Julia Schulze & Selwyn Piramuthu & Wei Zhou, 2023. "IoT and Blockchain Based Framework for Logistics in Food Supply Chains," Information Systems Frontiers, Springer, vol. 25(5), pages 1743-1756, October.
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