A Pareto-Based Clustering Approach for Solving a Bi-Objective Mobile Hub Location Problem with Congestion

Maryam Dehghan Chenary, Arman Ferdowsi and Richard F. Hartl ()
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Maryam Dehghan Chenary: Department of Business Decisions and Analytics, University of Vienna, 1090 Wien, Austria
Arman Ferdowsi: ECS Group, TU Wien, 1040 Wien, Austria
Richard F. Hartl: Department of Business Decisions and Analytics, University of Vienna, 1090 Wien, Austria

Logistics, 2024, vol. 8, issue 4, 1-30

Abstract: Background : This paper introduces an enhanced multi-period p -mobile hub location model that accounts for critical factors such as service time, flow processing delays, and congestion impacts at capacity-constrained hubs. As (urban) transportation networks evolve, mobile hubs play an increasingly vital role in promoting sustainable logistics solutions and addressing complex operational challenges. By enabling the repositioning of hubs across periods, this model seeks to minimize overall costs, particularly in response to dynamic demand fluctuations. Method : To solve this problem, we propose a bi-objective optimization model and introduce a hybrid meta-heuristic algorithm tailored to this application. The algorithm involves a clustering-based technique for evaluating solutions and a refined genetic approach for producing new sets of solutions. Results : Various experiments have been conducted on the Australian Post dataset to evaluate the proposed method. The results have been compared with Multiple-Objecti-ve Particle Swarm Optimization (MOPSO) and Non-Domi-nated Sorting Genetic Algorithm (NSGA-II) using several performance evaluation metrics. Conclusions : The results indicate that the proposed algorithm can provide remarkably better Pareto sets than the other competitive algorithms.

Keywords: mobile hub location problem; multi-objective optimization; congestion; service time; meta-heuristic approach (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2024
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