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A discrete particle swarm optimisation for operation sequencing in CAPP

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  • Jianping Dou
  • Jun Li
  • Chun Su

Abstract

Operation sequencing is one of crucial tasks for process planning in a CAPP system. In this study, a novel discrete particle swarm optimisation (DPSO) named feasible sequence oriented DPSO (FSDPSO) is proposed to solve the operation sequencing problems in CAPP. To identify the process plan with lowest machining cost efficiently, the FSDPSO only searches the feasible operation sequences (FOSs) satisfying precedence constraints. In the FSDPSO, a particle represents a FOS as a permutation directly and the crossover-based updating mechanism is developed to evolve the particles in discrete feasible solution space. Furthermore, the fragment mutation for altering FOS and the uniform and greedy mutations for changing machine, cutting tool and tool access direction for each operation, along with the adaptive mutation probability, are adopted to improve exploration ability. Case studies are used to verify the performance of the FSDPSO. For case studies, the Taguchi method is used to determine the key parameters of the FSDPSO. A comparison has been made between the result of the proposed FSDPSO and those of three existing PSOs, an existing genetic algorithm and two ant colony algorithms. The comparative results show higher performance of the FSDPSO with respect to solution quality for operation sequencing.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:11:p:3795-3814
    DOI: 10.1080/00207543.2018.1425015
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    Citations

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    Cited by:

    1. 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.
    2. Oscar Alberto Alvarez-Flores & Raúl Rivera-Blas & Luis Armando Flores-Herrera & Emmanuel Zenén Rivera-Blas & Miguel Angel Funes-Lora & Paola Andrea Niño-Suárez, 2024. "A Novel Modified Discrete Differential Evolution Algorithm to Solve the Operations Sequencing Problem in CAPP Systems," Mathematics, MDPI, vol. 12(12), pages 1-20, June.
    3. Yuming Guo, 2023. "Towards the efficient generation of variant design in product development networks: network nodes importance based product configuration evaluation approach," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 615-631, February.
    4. Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    5. Chunghun Ha, 2020. "Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 523-560, September.
    6. Luo, Kaiping & Shen, Guangya & Li, Liheng & Sun, Jianfei, 2023. "0-1 mathematical programming models for flexible process planning," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1160-1175.

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