Optimizing a decomposed high-speed rail multi-station shortest sequence problem
High-speed rail (HSR) planning requires optimizing station locations and routes to balance utility, ridership, operational efficiency, and cost-effective construction. Existing studies often rely on single-heuristic and simultaneous optimization approaches that assume predetermined station sequences...
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| Vydáno v: | Expert systems with applications Ročník 283; s. 127543 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
15.07.2025
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| Témata: | |
| ISSN: | 0957-4174 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | High-speed rail (HSR) planning requires optimizing station locations and routes to balance utility, ridership, operational efficiency, and cost-effective construction. Existing studies often rely on single-heuristic and simultaneous optimization approaches that assume predetermined station sequences while overlooking real-world geographic constraints and utility factors. This study introduces a novel nonlinear optimization model to optimize both HSR multi-station locations and shortest sequence/routes (HSR-MSL-SS) and addresses key limitations of existing methods. This model integrates three main objectives: maximizing station location utility and ridership and minimizing overall corridor length, subject to station location and corridor constraints. The problem is decomposed or divided into two interrelated sub-problems: multi-station location selection and shortest sequence determination. A hybrid metaheuristic optimization algorithm integrating multi-swarm particle swarm optimization (MPSO) and ant colony optimization (ACO) is developed to solve these sub-problems iteratively. Two hybridization strategies are presented: Se-MPSO-ACO, which sequentially optimizes multi-station location and routing, and Si-MPSO-ACO, which optimizes both sub-problems simultaneously. As per the author’s knowledge, this is the first attempt at solving the HSR-MSL-SS problem using a decomposed and hybrid swarm intelligence-based approach. A real-world HSR case study validates the approach. The Se-MPSO-ACO and Si-MPSO-ACO achieved 10.8% and 7.8% improvement in objective function values, respectively, over the expert-designed alternative. Sensitivity analysis indicates that network connectivity and population accessibility have the most significant impact on total utility. Convergence analysis revealed that while both methods perform well, the Si-MPSO-ACO is more responsive to changes in weight factors and demonstrates greater stability towards convergence. Se-MPSO-ACO provides robust solutions with lower computational complexity. |
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| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2025.127543 |