A parallel multi-objective genetic algorithm with learning based mutation for railway scheduling. (April 2019)

• Record Type: Journal Article
• Authors: Nitisiri, Krisanarach; Gen, Mitsuo; Ohwada, Hayato
• Is Part Of: Computers & industrial engineering. Volume 130(2019)
• Page Start: 381
• Page End: 394
• Publication Date: 2019-04
• Subjects: Railway scheduling -- Multi-objective genetic algorithm -- Parallel computation -- CUDA; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2019.02.035
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A parallel multi-objective EA is developed for the railway scheduling problem. A learning-based mutation is incorporated into the algorithm. The algorithm is compared with the other sequential MoEAs on the CPU level. The best compromised solution is selected from Pareto optimization solutions set. The compromised schedule with less computational time can be obtained. Abstract: Railway system is a reliable and efficiency major public transportation. It is supported by many countries since it has a less environmental effect compared to another type of transportation. As the railway networks have become larger and more complex with increasing passenger demand, both aspects from the passenger satisfaction and operational cost need to be satisfied. This paper proposes a Parallel Multi-objective Evolutionary Algorithm with Hybrid Sampling Strategy and learning-based mutation to solve the railway train scheduling problem. Learning techniques have been coupled with a multi-objective genetic algorithm to guide the search for better solutions. In this paper, we incorporate a learning-based algorithm into a mutation process. The evaluation process is divided into sub-process and calculated by a parallel computational unit using GPU CUDA framework. Two sets of numerical experiments based on a small-scale case of Thailand ARL transit line and a larger case of BTS transit network are implemented to verify the effectiveness of the proposed approaches. The experimental results show the effectiveness of the proposed algorithm comparing to sequential CPU computational and two classical multi-objective evolutionary algorithms. With the same number of operating trains, the proposed algorithm can obtain schedule with less average waiting time and the time used for computational is significantly reduced.