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Hybrid constrained permutation algorithm and genetic algorithm for process planning problem

Author

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  • Abdullah Falih

    (University of Baghdad)

  • Ahmed Z. M. Shammari

    (University of Baghdad)

Abstract

In this research, a hybrid constrained permutation algorithm and genetic algorithm approach is proposed to solve the process planning problem and to facilitate the optimisation process. In this approach, the process planning problem is represented as a graph in which operations are clustered corresponding to their machine, tool, and tool access direction similarities. A constrained permutation algorithm (CPA) developed to generate a set of optimised feasible operations sequences based on the principles of minimising the number of setup changes and the number of tool changes. Due to its strong capability in global search through multiple optima, genetic algorithm (GA) is used to search for an optimal or near optimal process plan, in which the population is initialised according to the operations sequences generated by CPA. Furthermore, to prevent premature convergence to local optima, a mixed crossover operator is designed and equipped into GA. Four comparative case studies are carried out to evidence the feasibility and robustness of the proposed CPAGA approach against GA, simulated annealing, tabu search, ant colony optimisation, and particle swarm optimisation based approaches reported in the literature, and the results are promising.

Suggested Citation

  • Abdullah Falih & Ahmed Z. M. Shammari, 2020. "Hybrid constrained permutation algorithm and genetic algorithm for process planning problem," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1079-1099, June.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:5:d:10.1007_s10845-019-01496-7
    DOI: 10.1007/s10845-019-01496-7
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    References listed on IDEAS

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    1. Yuliang Su & Xuening Chu & Dongping Chen & Xiwu Sun, 2018. "A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 313-332, February.
    2. Moon, Chiung & Kim, Jongsoo & Choi, Gyunghyun & Seo, Yoonho, 2002. "An efficient genetic algorithm for the traveling salesman problem with precedence constraints," European Journal of Operational Research, Elsevier, vol. 140(3), pages 606-617, August.
    3. Jianping Dou & Jun Li & Chun Su, 2018. "A discrete particle swarm optimisation for operation sequencing in CAPP," International Journal of Production Research, Taylor & Francis Journals, vol. 56(11), pages 3795-3814, June.
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    Cited by:

    1. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    2. Konstantinos S. Boulas & Georgios D. Dounias & Chrissoleon T. Papadopoulos, 2023. "A hybrid evolutionary algorithm approach for estimating the throughput of short reliable approximately balanced production lines," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 823-852, February.

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