This paper presents a hybrid evolutionary technique for solving train-scheduling problem. The proposed approach exploits key features of genetic algorithm (GA) and Tabu search (TS) in order to find nearly optimal feasible solution. Strategic oscillation is incorporated in GA to diversify the search space. The objective is to minimize the associated delays caused by train conflicts (cross or overtake) that is the waiting times at the train stations. The necessary algorithmic solution is described together with some empirical observations about the performance of our train-scheduling model. The simulation results show that our model is able to produce useful results in terms of minimizing delays and producing nearly optimal schedules.
An Effective Meta-Heuristic Technique for Solving Train Scheduling Problem
First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China
2007-07-09
Conference paper
Electronic Resource
English
An Effective Meta-Heuristic Technique for Solving Train Scheduling Problem
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