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

Amin Aghalari, Farjana Nur and Mohammad Marufuzzaman

International Journal of Production Economics, 2020, vol. 229, issue C

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%.

Keywords: Inland waterway port; Port optimization; Perishable products; Waterlevel fluctuation; Enhanced Benders decomposition algorithm; Sample average approximation (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:proeco:v:229:y:2020:i:c:s092552732030222x

DOI: 10.1016/j.ijpe.2020.107863

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