International Journal of Industrial Engineering & Production Research
Iran University of Science & Technology
Thu, Oct 29, 2026
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1- Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2- Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran ,M.Seifbarghy@alzahra.ac.ir
3- Department of Industrial Engineering, Chiang Mai University, Chiang Mai, Thailand
2- Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran ,
3- Department of Industrial Engineering, Chiang Mai University, Chiang Mai, Thailand
Abstract: (24 Views)
The growing size and complexity of hub location networks make large-scale problems computationally challenging and necessitate efficient solution methods capable of generating high-quality solutions within reasonable computational times. In this paper, two novel metaheuristic algorithms based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are developed to solve bi-objective single-allocation hub location problems. Multiple enhancements are introduced to improve the search process, including a heuristic population generation procedure, an adaptive mechanism for determining the number of hubs in the initial population, and an improved mutation operator designed to overcome the frozen-gene issue encountered in previous studies.
The proposed algorithms are applied to two bi-objective hub location models that were previously solved using an interactive approach (IA) only for small instances of the Australian Post (AP) data set. Computational experiments demonstrate that the proposed algorithms are effective in generating high-quality and diverse non-dominated solutions. In addition, they enable the solution of AP instances containing up to 200 nodes, extending the scope of the computational studies previously reported in the literature. The findings confirm the suitability of the proposed approaches for large-scale bi-objective hub location problems.
The proposed algorithms are applied to two bi-objective hub location models that were previously solved using an interactive approach (IA) only for small instances of the Australian Post (AP) data set. Computational experiments demonstrate that the proposed algorithms are effective in generating high-quality and diverse non-dominated solutions. In addition, they enable the solution of AP instances containing up to 200 nodes, extending the scope of the computational studies previously reported in the literature. The findings confirm the suitability of the proposed approaches for large-scale bi-objective hub location problems.
Type of Study: Research |
Subject:
Facilities Design & or Work Space Design
Received: 2025/09/6 | Accepted: 2026/10/26
Received: 2025/09/6 | Accepted: 2026/10/26
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