Academic Journal of Engineering and Technology Science, 2020, 3(8); doi: 10.25236/AJETS.2020.030809.
Lingqiao Chen1,*, Gengjun Gao2
1 Shanghai Maritime University Logistics Research Center, Shanghai 201306, China
2 Shanghai Maritime University Logistics Research Center, Shanghai 201306, China
*Corresponding Author
Aiming at the problem of uncertain parts processing time and delivery date in the scheduling of remanufacturing flexible job shops, triangular fuzzy numbers and trapezoidal fuzzy numbers are introduced to represent the uncertain processing time and delivery date respectively, and the maximum completion time is the minimum as the goal to establish a fuzzy model of workshop scheduling with the satisfaction constraint of the average delivery time of parts, and the model is transformed and finally solved by the MSOS coding genetic algorithm. And get: the positive or negative attitude of the decision maker will affect the decision result, the more negative the decision, the worse the result, on the contrary, the more positive the decision, the better the result; as the average delivery time satisfaction threshold gradually increases, the maximum completion time gradually extend.
fuzzy operation time, fuzzy delivery date, remanufacturing, flexible job shop scheduling, MSOS coding genetic algorithm
Lingqiao Chen, Gengjun Gao. Optimization of remanufacturing flexible job shop scheduling under uncertain environment. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 8: 77-89. https://doi.org/10.25236/AJETS.2020.030809.
[1] Huang Xuewen, Chen Shaofen, Zhou Tianyu, Sun Yuting. Summary of genetic algorithms for flexible job shop scheduling [J/OL]. Computer Integrated Manufacturing System:1-26[2020-12-02].
[2] Jamrus T, Chien CF, Gen M, et al. Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing[J]. IEEE Transactions on Semiconductor Manufacturing,2018,31( 1): 32-41.
[3] Xu Y, Wang L, Wang S Y, et al. An effective teaching–learning-based optimization algorithm for the flexible job-shop scheduling problem with fuzzy processing time[J]. Neurocomputing, 2015,148:260-268.
[4] Guo Jun, Zhong Jingcheng, Du Baigang, Wu Rui, Li Yibing. Multi-objective scheduling method for remanufacturing workshops considering fuzzy operation time[J]. Control and Decision, 2020, 35(06): 1497-1504.
[5] Hiroaki Ishii, Minoru Tada, Teruo Masuda. Two scheduling problems with fuzzy due-dates[J].Fuzzy Sets and Systems,1992,46(3):339-347.
[6] Cheng T C E. Optimal common due date with limited completion time deviation[J].Journal of the Operational Research Society,1988,15,91-96.
[7] Ma Jing, Li Yan. Research on IPPS under the condition of fuzzy delivery date[J]. Manufacturing Automation, 2019, 41(04): 24-29.
[8] Zhou Jing. Processing workshop scheduling problem based on random processing time and fuzzy delivery date [D]. Tsinghua University, 2010.
[9] Luo Yao, Gao Gengjun. Scheduling of remanufactured parts processing workshop under uncertain environment[J]. Journal of Chongqing Normal University (Natural Science Edition), 2018, 35(05): 26-32.
[10] Liu Qiong, Zhang Chaoyong, Rao Yunqing, Shao Xinyu. Improved genetic algorithm to solve flexible job shop scheduling problem[J]. Industrial Engineering and Management, 2009, 14(02): 59-66.
[11] Gao Gengjun, Luo Yao. Research on remanufacturing production scheduling under uncertain environment[J]. Operations Research and Management, 2019, 28(11): 185-190.