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A genetic algorithm for operation sequencing in CAPP using edge selection based encoding strategy

Author

Listed:
  • Yuliang Su

    (Shanghai Jiao Tong University)

  • Xuening Chu

    (Shanghai Jiao Tong University)

  • Dongping Chen

    (Shanghai Jiao Tong University)

  • Xiwu Sun

    (Shanghai Aerospace Equipment Manufacturer)

Abstract

Operation sequencing in CAPP aims at determining the optimal order of machining operations with minimal machining cost and satisfying all the precedence constraints. The genetic algorithm (GA) is widely used to solve precedence constrained operation sequencing problem (PCOSP) due to its efficiency and parallel processing capability. How to guarantee the precedence constraints is always a hot research topic and there are mainly two classes of methods. The first ones use additional adjustment approaches to repair the infeasible solutions that break precedence constraints. It is unreliable and low efficient. The second ones avoid infeasible solutions in initialization through some encoding approaches such as topological storing based encoding approach, but the premature convergence problem may occur facing some complicated PCOSPs. To solve these problems, an edge selection strategy based GA is proposed. The edge selection based strategy could produce feasible solutions in initialization, and assures that every feasible solution will be generated with acceptable probability so as to improve GA’s converging efficiency. Then the precedence constraints are kept by order crossover. Modified mutation operator is designed to optimize the selection of machine tool, tool access direction and cutting tool for each operation. The experiments illustrate that the proposed algorithm is effective and efficient.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:2:d:10.1007_s10845-015-1109-6
    DOI: 10.1007/s10845-015-1109-6
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    References listed on IDEAS

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    1. 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.
    2. Gilbert Laporte & Jorge Riera-Ledesma & Juan-José Salazar-González, 2003. "A Branch-and-Cut Algorithm for the Undirected Traveling Purchaser Problem," Operations Research, INFORMS, vol. 51(6), pages 940-951, December.
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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. 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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