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Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study

Author

Listed:
  • Jingya Wang

    (School of Economics and Management, Handan University, Handan 056005, China)

  • Jiusi Wen

    (School of Economics and Management, Handan University, Handan 056005, China)

  • Vukašin Pajić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

  • Milan Andrejić

    (Faculty of Transport and Traffic Engineering, University of Belgrade, Vojvode Stepe 305, 11000 Belgrade, Serbia)

Abstract

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions.

Suggested Citation

  • Jingya Wang & Jiusi Wen & Vukašin Pajić & Milan Andrejić, 2024. "Optimizing Cross-Dock Terminal Location Selection: A Multi-Step Approach Based on CI-DEA–IDOCRIW–MABAC for Enhanced Supply Chain Efficiency—A Case Study," Mathematics, MDPI, vol. 12(5), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:5:p:736-:d:1349078
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    References listed on IDEAS

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    1. Edmundas Kazimieras Zavadskas & Valentinas Podvezko, 2016. "Integrated Determination of Objective Criteria Weights in MCDM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 267-283, March.
    2. Milan Andrejić & Vukašin Pajić & Milorad Kilibarda, 2023. "Distribution Channel Selection Using FUCOM-ADAM: A Novel Approach," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    3. Milan Andrejić, 2023. "Modeling Retail Supply Chain Efficiency: Exploration and Comparative Analysis of Different Approaches," Mathematics, MDPI, vol. 11(7), pages 1-24, March.
    4. Amin Soleimaninanadegany & Adnan Hassan & Masoud Rahiminezhad Galankashi, 2017. "Product allocation of warehousing and cross docking: a genetic algorithm approach," International Journal of Services and Operations Management, Inderscience Enterprises Ltd, vol. 27(2), pages 239-261.
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