Open AccessA Study of Metro Organization Based on Multi-objective Programming and Hybrid Genetic Algorithm
Author(s) -
Jun Zhang,
Jiang Li,
Yan Wu
Publication year - 2017
Publication title -
periodica polytechnica. transportation engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.388
H-Index - 15
eISSN - 1587-3811
pISSN - 0303-7800
DOI - 10.3311/pptr.9586
Subject(s) - crossover , computer science , mathematical optimization , coding (social sciences) , genetic algorithm , vehicle routing problem , mode (computer interface) , convergence (economics) , headway , algorithm , routing (electronic design automation) , simulation , mathematics , computer network , statistics , artificial intelligence , economics , economic growth , operating system
Based on the train routing mode ascertained by suitability analysis, we construct a multi-objective problem to optimize the train routing, marshaling number and train headway from the perspective of general cost and segment load ratio by analyzing relevant characteristics like trip time, trip cost, operation cost, spatial distribution characteristics etc. Then the Singular Value Decomposition method and simulation software RailSys have been adopted to calibrate related parameters for the subsequent calculation. By comparison, the Genetic Algorithm is recommended to get an optimal solution to this multi-objective problem, and we improve the traditional algorithm by modifying the coding type, fitness function and crossover operation to enhance the efficiency and convergence. Finally, an operational mode which both satisfies the technological and passenger conditions has been identified to guarantee travellers’ safety, improve operation efficiency, save trip time and decrease cost.