Intelligent Scheduling of Automotive Stamping Workshops Based on an Enhanced Genetic Algorithm

Authors

  • Zhiyuan Li School of Intelligence Technology, Geely University of China, China
  • Desheng Wu Zhejiang Geely New Energy Commercial Vehicle Group Co., LTD, China
  • Cheng He Zhejiang Geely New Energy Commercial Vehicle Group Co., LTD, China
  • XiaoFeng Gan Zhejiang Geely New Energy Commercial Vehicle Group Co., LTD, China
  • Peichun Chen Research Services, Coventry University, Coventry, UK

DOI:

https://doi.org/10.15837/ijccc.2025.1.6874

Keywords:

Automotive Stamping Workshop, Intelligent Scheduling, Enhanced Genetic Algorithm, Optimization Scheduling.

Abstract

With the rapid development of the automotive manufacturing industry, enhancing production efficiency and flexibility in stamping workshops has become crucial for maintaining competitiveness. This paper presents an intelligent scheduling method based on an enhanced genetic algorithm to address the scheduling challenges in automotive stamping workshops. By incorporating the magic square selection method for initializing the population and an improved roulette wheel selection operation, this approach significantly enhances the search efficiency and solution quality of the genetic algorithm. During the algorithm’s implementation, innovative crossover methods and decoding mechanisms were designed to better accommodate the production characteristics and constraint conditions of stamping workshops. The proposed method was validated using production data from an automotive factory. The scheduling results demonstrate that the enhanced genetic algorithm effectively addresses over 95% of the scheduling issues encountered in intelligent scheduling of automotive stamping workshops. The algorithm markedly improves workshop production efficiency, reduces production cycles, and enhances the stability and feasibility of production plans. This study provides a robust technical solution for intelligent production management in automotive stamping workshops, offering substantial theoretical significance and practical application value.

Author Biographies

Cheng He, Zhejiang Geely New Energy Commercial Vehicle Group Co., LTD, China



XiaoFeng Gan, Zhejiang Geely New Energy Commercial Vehicle Group Co., LTD, China



References

Bektas T. (2004). The multiple traveling salesman problem: an overview of formulations and solution procedures, Omega, 34(3), 209-219, 2004. https://doi.org/10.1016/j.omega.2004.10.004

Cheng R.W.; Mitsuo Gen.; Yasuhiro Tsujimura. (1999). A Tutorial Survey of Job-shop Scheduling Problems Using Genetic Algorithms Part II: Hybird Genetic Search Strategies, Computers and Industrial Engineering, 36(2), 343-364, 1999. https://doi.org/10.1016/S0360-8352(99)00136-9

Das, S.; Suganthan, P.N. (2011). Differential evolution: A survey of the state-of-the-art, IEEE Transactions on Evolutionary Computation, 15(1), 4-31, 2011. https://doi.org/10.1109/TEVC.2010.2059031

D.N. Zhou.; V Cherkassky.; T.R. Baldwin. (1990). Scaling Neural Network for Job-shop Scheduling, International Joint Conference on Neural Networks, USA: IEEE, 889-894, 1990. https://doi.org/10.1109/IJCNN.1990.137947

Gao K.Z.; Gao Z.G.; Zhang L. (2019). A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems, IEEE/CAA Journal of Automatica Sinica, 6(4), 904-916, 2019. https://doi.org/10.1109/JAS.2019.1911540

G Buxey. (1989). Production Scheduling: Practice and Theory, European Journal of Operational Research, 39, 17-311, 1989. https://doi.org/10.1016/0377-2217(89)90349-4

H.B. Yu.; W Liang. (2001). Neural Network and Genetic Algorithm based Hybrid Approach to Expanded Job-shop Scheduling, Computers & Industrial Engineering, 39(3-4), 337-356, 2001. https://doi.org/10.1016/S0360-8352(01)00010-9

H Jeong.; Jinwoo Park.; R C Leachman. (1999). A Batch Splitting Method for A Job Shop Scheduling Problem in an MRP Environment, International Journal of Production Research, 37(15), 3583-3598, 1999. https://doi.org/10.1080/002075499190194

I Sabuncuolu.; B Gurgun. (1996). A Neural Network Model for Scheduling Problems, European Journal of Operational Research, 93(2), 288-299, 1996. https://doi.org/10.1016/0377-2217(96)00041-0

J B Lasserre. (1992). An Integrated Model for Job-shop Planning and Scheduling, Management Science, 38(8), 1201-1211, 1992. https://doi.org/10.1287/mnsc.38.8.1201

Jiang T H.; Deng G L. (2018). Optimizing the low-carbon flexible job shop scheduling problem considering energy consumption, IEEE Access, 6, 46346-46355, 2018. https://doi.org/10.1109/ACCESS.2018.2866133

JK Lenstra.; AHG Kan.; P Brucker. (1977). Complexity of Machine Scheduling Problems, Studies in Integer Programming, 343-361, 1977. https://doi.org/10.1016/S0167-5060(08)70743-X

J M van Laarhoven.; HL Aarts. (1992). Jan Karel Lenstra. Job Shop Scheduling by Simulated Annealing, Operations Research, 40(1), 113-125, 1992. https://doi.org/10.1287/opre.40.1.113

Li K.; Deb K.; Zhang Q. (2019). An evolutionary many-objective optimization algorithm based on dominance and decomposition, IEEE Transactions on Evolutionary Computation, 23(4), 605- 617, 2019.

Li K.; Zhang Q. (2019). An efficient and robust evolutionary algorithm for real-parameter optimization, IEEE Transactions on Evolutionary Computation, 13(3), 451-466, 2019.

PHU-ANG A. (2021). An improve artificial immune algorithm for solving the travelling salesman problem, Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunication Engineering, 261-264, 2021. https://doi.org/10.1109/ECTIDAMTNCON51128.2021.9425773

S G Ponnambalam.; N Jawahar.; P Aravindan. (1999). A simulated Annealing Algorithm for Job Shop Scheduling, Production Planning & Control, 10(8), 767-777, 1999. https://doi.org/10.1080/095372899232597

TANG K.; Liu S.; YANG P. (2021). Few-shots parallel algorithm portfolio construction via coevolution, IEEE Transactions on Evolutionary Computation, 25(3), 595-607, 2021. https://doi.org/10.1109/TEVC.2021.3059661

Wang Y.; Cai Z.; Zhang Q (2019). Differential evolution with dynamic stochastic selection for constrained optimization, IEEE Transactions on Evolutionary Computation, 14(4), 678-696, 2019.

Wang Y H.; FU L Q.; SU Y Q. (2018). Genetic algorithm in flexible workshop scheduling based on multi-objective optimization, Journal of Interdisciplinary Mathematics, 21(5), 1249-1254, 2018. https://doi.org/10.1080/09720502.2018.1495398

Wang Y.; Yang O.; Wang S N. (2019). A solution to single-macgine inverse job-Shop scheduling problem, Journal Citation Reports, 18(2), 335-342, 2019. https://doi.org/10.2507/IJSIMM18(2)CO7

WU C.; FU X, PEI. (2021). A novel sparrow search algorithm for the traveling salesman problem, IEEE Access, 9, 153456, 2021. https://doi.org/10.1109/ACCESS.2021.3128433

YUAN F.; SUN H.; Kang L. (2022). Joint optimization of train scheduling and dynamic passenger flow control strategy with headway-dependent demand, Transportmetrica, 10(1), 627-651, 2022. https://doi.org/10.1080/21680566.2022.2025951

Zhang J.; Ding G.; Zou Y. (2019). Review of job shop scheduling research and its new perspectives under Industry 4.0, Journal of Intelligent Manufacturing, 30(4), 1809-1830, 2019. https://doi.org/10.1007/s10845-017-1350-2

Zhang R.; Chiong R. (2016). Solving the energy-efficient job shop scheduling problem: a multiobjective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption, Journal of Cleaner Production,, 112, 3361-3375, 2016. https://doi.org/10.1016/j.jclepro.2015.09.097

Additional Files

Published

2025-01-03

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.