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Process configuration based on generative constraint satisfaction problem

Author

Listed:
  • Lin Wang

    (Harbin Institute of Technology)

  • Shi-Sheng Zhong

    (Harbin Institute of Technology)

  • Yong-Jian Zhang

    (Harbin Institute of Technology at Weihai)

Abstract

Product configuration, a widely used technology in product family design, is one of the most effective technologies of mass customization strategies which have been deployed by many companies for years. Nevertheless, the mass customization needs to cover the management of the whole customizable product cycle. In order to assist the development of mass customization, it is essential to extend the configuration technology to product family process planning, which is the technological essence of process configuration. In this article the process configuration task is confirmed based on the analysis of characteristics of process planning. Compared with the solving scheme of product configuration, the process configuration is then mapped into a generative constraint satisfaction problem (GCSP), and the variables and constraints of the process configuration GCSP model are identified respectively. An algorithm based on backtracking algorithm is introduced to complete the process configuration. Finally, an experiment on machining process configuration for satellite plate panel verifies the validity of our algorithm.

Suggested Citation

  • Lin Wang & Shi-Sheng Zhong & Yong-Jian Zhang, 2017. "Process configuration based on generative constraint satisfaction problem," Journal of Intelligent Manufacturing, Springer, vol. 28(4), pages 945-957, April.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:4:d:10.1007_s10845-014-1031-3
    DOI: 10.1007/s10845-014-1031-3
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    References listed on IDEAS

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    1. D. Wu & L. Zhang & J. Jiao & R. Lu, 2013. "SysML-based design chain information modeling for variety management in production reconfiguration," Post-Print hal-00846436, HAL.
    2. L. Zhang & B. Rodrigues, 2010. "Nested colored timed Petri nets for production configuration of product families," Post-Print hal-00798992, HAL.
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    Cited by:

    1. Marcin Relich, 2023. "A Data-Driven Approach for Improving Sustainable Product Development," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    2. Lexie Lan Huang & Rocky Peng Chen & Kimmy Wa Chan, 2024. "Pairing up with anthropomorphized artificial agents: Leveraging employee creativity in service encounters," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 955-975, July.
    3. Zhang, Linda L. & Lee, Carman K.M. & Akhtar, Pervaiz, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," International Journal of Production Economics, Elsevier, vol. 229(C).
    4. Linda Zhang & Carman K.M. Lee & Pervaiz Akhtar, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," Post-Print hal-03276827, HAL.

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