Multi-objective metaheuristics for hub location problem: A case study of rail network planning

Abstract

Purpose: Hub location problems have been utilized in various applications including rail transportation network planning, where hub serves as a key transit point within the network. In this paper, we focus on determining the optimal location for a rail transportation hub, where cost and service are trade-off.

Design/methodology/approach: The problem is formulated as a multi-objective programming model with the objectives of minimizing total transportation costs and minimizing maximum travel time. A case study of rail transportation network hub planning in Thailand is presented. Given the complexity and large scale of the real-world case study, we develop and compare the Multi-Objective Tabu Search (MOTS) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem.

Findings: The proposed algorithms yield efficient performance in terms of computational time and solution quality. Performance comparison is further analyzed to see the difference in both algorithms.

Originality/value: The results offer valuable managerial insights for decision-makers in rail transportation hub network design.