Academic Journal of Computing & Information Science, 2022, 5(11); doi: 10.25236/AJCIS.2022.051101.
Yujie Shen
Shanghai Maritime University, Shanghai, 200120, China
In this paper, the traditional genetic algorithm is improved, through MSOS coding, combined with IPOX and MPX adaptive crossover operators, the fitness function is improved, the adaptive crossover mutation probability function is introduced, and the simulation experiments are carried out on two standard instances of MK01 and 8×8. The solution of the 8×8 standard instance with the traditional genetic algorithm verifies the effectiveness and superiority of the improved genetic algorithm proposed in this paper.
Flexible job-shop Scheduling, Genetic Algorithm, Algorithm improving, MSOS
Yujie Shen. Application of Improved Genetic Algorithm in FJSP. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 11: 1-9. https://doi.org/10.25236/AJCIS.2022.051101.
[1] Xu Hua, Cheng Bing. Hybrid Genetic Bat Algorithm for Single-Objective Flexible Job Shop Scheduling Problem [J]. Journal of Chinese Computer Systems, 2018, 39(5): 1010-1015.
[2] Chaudhry I A, Khan A A. A research survey: review of flexible job shop scheduling techniques [J]. International Transactions in Operational research, 2016, 23(3): 551-591.
[3] Driss I, Mouss K N, Laggoun A. A new genetic algorithm for flexible job-shop scheduling problems [J]. Journal of Mechanical Science and Technology, 2015, 29(3): 1273-1281.
[4] Li X Y, Gao L. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem [J]. International Journal of Production Economics, 2016, 174: 93-110.
[5] Li Shanghan, Hu Rong, Qian Bin etc. A Hyperheuristic Genetic Algorithm for Fuzzy Flexible Job Shop Scheduling [J]. Control Theory & Applications, 2020, 37(2): 316-330.
[6] Michael L. Pinedo. Scheduling [M]. New York, NY, USA: Springer Science+Business Media, LLC, 2016: 13-19.
[7] Li Qing, Wei Guagncun, Gao Lan, Qiu Guohua, Xiao Xinguang. An Improved Genetic Algorithm for Solving TSP Problems [J]. Software Guide, 2020, 19(03): 116-119.
[8] Gao Liang, Zhang Guohui, Wang Xiaojuan. Flexible Job Shop Scheduling Intelligent Algorithm and Its Application [M]. Wuhan: Huazhong University of Science and Technology Press, 2012.
[9] Chen Jinguang, Ma Lingye, Ma Lili. An Improved Genetic Algorithm for Solving Job Shop Scheduling Problem [J]. Computer Systems & Applications, 2021, 30(05): 190-195.
[10] Zhang Chaoyong, Liu Qiong, Qiu Haobo, Shao Xinyu. Research on Flexible job-shop Scheduling Problem Considering Processing Cost and Time [J]. Mechanical Science and Technology for Aerospace Engineering, 2009, 28(08): 1005-1011.
[11] Zheng Xianpeng, Wang Lei. Improved Genetic Algorithm for Job Shop Scheduling Problem [J]. Journal of Hebei University of Science and Technology, 2019, 40(06): 496-502.