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A Bender’s based nested decomposition algorithm to solve a stochastic inland waterway port management problem considering perishable product

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  • Aghalari, Amin
  • Nur, Farjana
  • Marufuzzaman, Mohammad

Abstract

The inland waterway transportation system provides one of the most economical and environmentally friendly means of transportation with significant contributions to the nation’s overall transportation economy. This study aims to develop a sound and realistic model, capturing diversified inland waterway transportation network-related properties and complex interactions between different transportation entities. Additionally, this study ensures optimal inventory management decisions for perishable products having stochastic availability under unpredictable waterway conditions over time. To this end, we propose a two-stage mixed-integer linear programming (MILP) model capturing the aforementioned issues, along with specific concern to the perishable product storage and transportation. Subsequently, we propose a hybrid decomposition algorithm combining the enhanced Benders decomposition algorithm and sample average approximation to solve the large size test instances of this complex problem. Further, a case study considering the inland waterway transportation system of the lower Mississippi River is demonstrated. The sensitivity analysis results show that the system is highly sensitive to the commodity shelf life. With a 60% higher commodity deterioration rate, the overall commodity storage need increases by 12.3%, the total system cost increases by about 33%.

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  • Aghalari, Amin & Nur, Farjana & Marufuzzaman, Mohammad, 2020. "A Bender’s based nested decomposition algorithm to solve a stochastic inland waterway port management problem considering perishable product," International Journal of Production Economics, Elsevier, vol. 229(C).
    • Handle: RePEc:eee:proeco:v:229:y:2020:i:c:s092552732030222x
    DOI: 10.1016/j.ijpe.2020.107863
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    Cited by:

    1. Cao, Yunzhi & Zhu, Xiaoyan & Yan, Houmin, 2022. "Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 163(C).
    2. Aghalari, Amin & Nur, Farjana & Marufuzzaman, Mohammad, 2021. "Solving a stochastic inland waterway port management problem using a parallelized hybrid decomposition algorithm," Omega, Elsevier, vol. 102(C).
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    4. Zhang, Haifeng & Yang, Kai & Gao, Yuan & Yang, Lixing, 2022. "Accelerating Benders decomposition for stochastic incomplete multimodal hub location problem in many-to-many transportation and distribution systems," International Journal of Production Economics, Elsevier, vol. 248(C).

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