Abstract
In the wafer manufacturing process of micro electro mechanical systems (MEMS), there are reentrant flow, parallel machines, and assembly operation. Therefore, this study models its scheduling problem as a reentrant flexible assembly job shop scheduling problem. First, a mathematical model is formulated to minimize the total tardiness and the total energy consumption. Second, an improved non-dominated sorting genetic algorithm II (INSGA-II) is proposed to solve this NP-hard problem. An encoding and decoding method are designed according to the problem characteristics. A rule-based initialization strategy is developed to improve the quality of the initialized population. Specific crossover, mutation and selection operators are designed. Finally, numerical experiments are carried out, and the result shows that the proposed algorithm can effectively solve the problem.
Supported by the National Natural Science Foundation of China (No. 52175449).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tay, F.E.H., Lee, L.H., Wang, L.: Production scheduling of a MEMS manufacturing system with a wafer bonding process. J. Manuf. Syst. 21(4), 287–301 (2002)
Han, Y.: Development of MEMS sensors (in Chinese). Electron. World J. 26(01), 4–8 (2019)
Guo, C., Zhibin, J., Zhang, H., Li, N.: Decomposition-based classified ant colony optimization algorithm for scheduling semiconductor wafer fabrication system. Comput. Ind. Eng. 62(1), 141–151 (2012)
Wu, X., Cao, Z.: An improved multi-objective evolutionary algorithm based on decomposition for solving re-entrant hybrid flow shop scheduling problem with batch processing machines. Comput. Ind. Eng. 169, 108236 (2022)
Wu, X., Xie, Z.: Heterogeneous distributed flow shop scheduling and reentrant hybrid flow shop scheduling in seamless steel tube manufacturing. In: Pan, L., Pang, S., Song, T., Gong, F. (eds.) BIC-TA 2020. CCIS, vol. 1363, pp. 78–89. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-1354-8_8
Mittler, M., Purm, M., Gihr, O.: Set management: minimizing synchronization delays of prefabricated parts before assembly. In: 1995 Winter Simulation Conference Proceedings, pp. 829–836 (1995)
Dong, J., Ye, C.: Research on green job shop scheduling problem of semiconductor wafers manufacturing with learning effect (in Chinese). Oper. Res. Manage. Sci. 30(04), 217–223 (2021)
Geng, K., Ye, C., Cao, L., Liu, L.: Multi-objective reentrant hybrid flowshop scheduling with machines turning on and off control strategy using improved multi-verse optimizer algorithm. Math. Probl. Eng. 2019, 2573873 (2019)
Zhao, S., Han, Q., Wang, G.: Product comprehensive scheduling algorithm based on virtual component level division coding. Comput. Integr. Manuf. Syst. 21, 2435–2445 (2015)
Garey, M.R., Johnson, D.S.: Computers and intractability: a guide to the theory of NP-completeness computer science. 1979338 (1979)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Guo, W., Lei, Q., Song, Y., Lyu, X.: A learning interactive genetic algorithm based on edge selection encoding for assembly job shop scheduling problem. Comput. Ind. Eng. 159, 107455 (2021)
Weifei, G., Qi, L., Yuchuan, S., Xiangfei, L., Lei, L.I.: Integrated scheduling algorithm of complex product with no-wait constraint based on virtual component. J. Mech. Eng. 56, 246 (2020)
Yan, X., Wu, X.: IMOEA/D to optimize job release problem for a reentrant hybrid flow shop. Comput. Ind. Eng. 163, 107800 (2022)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Wu, X., Zhang, Y., Zhao, K. (2023). An Improved NSGA-II for Solving Reentrant Flexible Assembly Job Shop Scheduling Problem. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13968. Springer, Cham. https://doi.org/10.1007/978-3-031-36622-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-36622-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36621-5
Online ISBN: 978-3-031-36622-2
eBook Packages: Computer ScienceComputer Science (R0)