Research Article of International Journal of Natural Science and Reviews
Motor Vehicle Maintenance Scheduling Model Based on Genetic algorithm
Yishuai Tian1*, Mingxuan Lou1, Xinhai Zhao1, Yalong Zheng1
1College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China.
To solve the problem of EMU maintenance planning time, we established a hybrid linear programming model based on adaptive genetic algorithm. According to the different levels of bullet train maintenance and the different arrival time, the constraint conditions are found, and the overall minimum maintenance time is taken as the objective function. Then the problem is transformed into a hybrid Flow-shop scheduling problem with unique process constraints, and an adaptive genetic algorithm is used to solve it again. It is concluded that the total maintenance takes 1125 minutes, and no blockage occurred in all EMU during the maintenance period. To provide a consistent basis for bullet train maintenance time and scheduling.
Keywords: Linear programming; Genetic algorithm; Hybrid; Flow-shop; Train maintenance
How to cite this article:
Yishuai Tian, Mingxuan Lou, Xinhai Zhao, Yalong Zheng. Motor Vehicle Maintenance Scheduling Model Based on Genetic algorithm. International Journ-al of Natural Science and Reviews, 2020; 5:13. DOI: 10.28933/ijnsr-2020-05-1005
1. Fei Gao, Yu Pan Beijing EMU maintenance base and EMU maintenance[J] Railway rolling stock, 2010,30 (04): 77-81
2. Meihua Zhu Research on optimization of the use of EMU under passenger dedicated line[D] Lanzhou Jiaotong University, 2013
3. Jian Li Research on the comprehensive optimi-zation method of EMU operation and mainte-nance plan [D] Beijing Jiaotong University, 2017
4. Maroti G, Kroon L. Maintenance routing for train units: The interchange model[J]. COMPUTERS & OPERATIONS RESEARCH,2007,34(4):1121-1140.
5. Zhang D, Hu H, Liu Y, et al. Railway Train Wheel Maintenance Model and Its Application[J]. TRANSPORTATION RESEARCH RECORD, 2014(2448):28-36.
6. Hani Y, Amodeo L, Yalaoui F, et al. Simulation based optimization of a train maintenance facil-ity[J]. JOURNAL OF INTELLIGENT MANUFAC-TURING, 2008,19(3):293-300.
7. Reich D. A linear programming approach for lin-ear programs with probabilistic constraints[J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2013,230(3):487-494.
8. Ding C, Chen L, Zhong B. Exploration of intelli-gent computing based on improved hybrid ge-netic algorithm[J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2019,224:S9037 -S9045.
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