1. A stochastic multi-objective optimization method for railways scheduling: a NSGA-II-based hybrid approach.
- Author
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Seifpour, Massoud, Asghari, Seyyed Amir, and Ghobaei-Arani, Mostafa
- Subjects
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TRAIN schedules , *MIXED integer linear programming , *METAHEURISTIC algorithms , *SIMULATED annealing , *GENETIC algorithms - Abstract
Optimizing resource utilization and train scheduling is essential to satisfy passengers and reduce operating costs. This study develops the train schedule under scenario-oriented stochastic conditions. The proposed approach is a multi-objective mathematical-based mixed integer linear programming (MILP) approach; the objective is to minimize the average passenger expectation and the total number of train operation cycles. The non-dominated sorting genetic algorithm (NSGA-II) has been developed with multi-crossover and multi-mutation operators, then hybrid with simulating annealing (SA) operator (NSGA-II-SA). The model with four meta-heuristic algorithms has been technically analyzed. In a case study, the train schedule in the double-track rail network of the Tehran–Mashhad railway of Iran has been compared with the golden point. Experimental results show that a proposed approach can suitably fit the problem considering important metrics with an improvement of %7.34 and %6.89 for the average passenger waiting time and the total number of train operation cycles, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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