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Academic Journal of Engineering and Technology Science, 2020, 3(6); doi: 10.25236/AJETS.2020.030605.

Multi-objective scheduling in remanufacturing workshop under uncertain environment

Author(s)

Lingqiao Chen1,*, Gengjun Gao2

Corresponding Author:
Lingqiao Chen
Affiliation(s)

1 Shanghai Maritime University, Shanghai 201306, China
2 Shanghai Maritime University, Shanghai 201306, China
*Corresponding Author

Abstract

Reasonable remanufacturing workshop scheduling can not only effectively improve the economic benefits of remanufacturing enterprises, but also bring good social benefits. The scheduling process of remanufacturing workshops is often affected by uncertain factors, and enterprises need to weigh and consider multiple optimization indicators of scheduling. In order to study the multi-index scheduling optimization problem of the remanufacturing workshop based on the uncertainty of quality status and processing time of waste parts, random numbers and triangular fuzzy numbers are used to represent the uncertainty of quality status and processing time. The maximum completion time, the processing cost and the minimum machine load balance are the goals. A multi-objective scheduling model with fuzzy operating time is established. The fuzzy multi-objective planning is transformed into a clear multi-objective planning through model conversion, and then solved by the NSGA-II algorithm. Using AHP method to make multi-objective decision-making, select the most satisfactory scheduling plan from the pareto optimal solution set. Finally, the correctness of the model and the effectiveness of the algorithm are verified by simulation examples. The result is: considering the uncertainty of the quality status of the remanufactured parts has a better control over the processing time. Compared with not considering the uncertainty of the quality status, the maximum completion time can be greatly reduced; the decision maker can give it according to his own preference Judgment matrix of the importance of each index, so as to select the most satisfactory scheduling plan in the pareto optimal solution set.

Keywords

uncertainty, remanufacturing, multi-objective scheduling

Cite This Paper

Wenhao HUANG. Multi-objective scheduling in remanufacturing workshop under uncertain environment. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 6: 35-45. https://doi.org/10.25236/AJETS.2020.030605.

References

[1] Liu Mingzhou, Zhang Xi, Liu Conghu, Zhang Mingxin, Ge Maogen. Production scheduling optimization method for remanufacturing workshop under uncertain environment[J]. Journal of Mechanical Engineering, 2014, 50(10): 206-212.
[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 Trans 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] Cui Shaodong, Chen Weida. Scrap steel remanufacturing production scheduling considering carbon emissions and variable processing time[J]. Industrial Engineering and Management, 2016, 21(03): 40-49.
[5] Rui Zhang,S.K. Ong,A.Y.C. Nee. A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling[J]. Elsevier B.V.,2015,37.
[6] Zhang Mingxin, Zhang Xi, Peng Jiangang, Chen Honghai. Multi-objective scheduling optimization method for remanufacturing workshop under uncertain environment[J]. Journal of Hefei University of Technology (Natural Science Edition), 2016, 39(04): 433-439+ 542.
[7] GAO KZ, SUGANTHAN PN, PAN QK, et al. An improved artificial bee colony algorithm for flexible job-shop scheduling problem with fuzzy processing time[J]. Expert Systems with Applications An International Journal, 2016, 65(C) : 52-67.
[8] ZHANG G, SHAO X, LI P, et al. An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem[J]. Computers & Industrial Engineering, 2009, 56(4): 1309- 1318.
[9] BLAZEWICZ J, FINKE G, HAOPT G. New trends in machine scheduling[J]. European Journal of Operational Research, 1988, 37: 303-317.
[10] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multi-objective genetic algorithms: NSGA- [J]. Ⅱ IEEE Trans. on Evolutionary Computation, 2002, 6(2): 182-197.
[11] Guo Jun, Zhong Jingcheng, Du Baigang, Wu Rui, Li Yibing. Research on multi-objective scheduling method of remanufacturing workshop considering fuzzy operation time[J/OL]. Control and Decision: 1 -7[2020-03- 22]. https://doi.org/10.13195/j.kzyjc.2018.1187.