Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling

Xu Zhang, Zhixue Liao, Lichao Ma and Jin Yao ()
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Xu Zhang: Sichuan University
Zhixue Liao: Southwestern University of Finance and Economics
Lichao Ma: Sichuan University
Jin Yao: Sichuan University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 1, No 12, 223-246

Abstract: Abstract To adapt to the flexibility characteristics of modern manufacturing enterprises and the dynamics of manufacturing subsystems, promote collaboration in manufacturing functions, and allocate production resources in a reasonable manner, a mathematical model of integrated process planning and scheduling (IPPS) problems was developed to optimize the global performance of manufacturing systems. To solve IPPS problems, a hierarchical multistrategy genetic algorithm was developed. To address the multidimensional flexibility of IPPS problems, a chromosome coding method was designed to include a scheduling layer, a process layer, a machine layer, and a logic layer. Multiple crossover operators and mutation operators with polytypic global or local optimization strategies were used during the genetic operation stage to expand the algorithm’s search dimension and maintain the population’s diversity, thereby addressing the problems of population evolution stagnation and premature convergence. The effectiveness of the algorithm was verified by benchmark testing in the example simulation process. The test data show that if the makespan is taken as the optimization target, the proposed genetic algorithm performs better in solving IPPS problems with high complexity. The use of multistrategy genetic operators and logic layer coding makes a significant contribution to the improved performance of the algorithm reported in this paper.

Keywords: Process planning; Jopshop scheduling; Multistrategy; Genetic algorithm (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10845-020-01659-x

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