Developing a hierarchical hub location model for sustainable supply chain network

Document Type : Article

Authors

1 Department of Industrial Engineering, University of Sistan and Bluchestan, Zahedan, Iran.

2 Department of Mathematics, Faculty of Mathematics, University of Sistan and Baluchestan, Zahedan, Iran.

Abstract

Identifying the optimal location for facilities is a key strategic objective for companies striving to enhance their competitiveness. Managers carefully select facility locations to ensure they effectively meet demand and align with organizational goals. Given the impracticality of establishing direct communication between all points in a network, utilizing hub points within networks can result in significant cost savings. Hub location problems are one of the new and remarkable topics in industrial engineering and one of the most important branches of transportation which is widely used in strategic areas such as transportation systems, postal systems, and communication networks. The use of hubs in the distribution network reduces the costs of current transmission in the network and thus increases system efficiency. In summary, hubs are used in different places of the supply chain such as transferring from point to point, sorting, and switching. The problem of location-allocation of hub is one important problem that is common in many transportation systems. One of the important branches of hub area is hierarchical hub that has been considered by many researchers. In this research, a two-objective model for the hierarchical hub location problem is presented. Given the importance of real-world environmental problems and concerns about increasing destructive environmental pollution, in this study, in addition to reviewing and trying to improve and reduce costs, environmental problems and their improvement have been studied. The proposed model also examines multi-mode transport and creates several types of transport systems in one hub. In the following, smaller problems are solved by GAMS software and large-scale problems are solved by genetic, strong Pareto and gray wolf metaheuristic algorithms and the results are compared. The results of solving problems with different dimensions show the good performance of the proposed algorithm, so that by using this method in an acceptable time, a suitable quality answer can be obtained.

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Main Subjects

References

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University of Sistan and Baluchestan. [In Persian].
Sharif Journal of Industrial Engineering & Management
Volume 40, Issue 2
March 2025
Pages 16-29

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