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An empirical study of collaborative capacity evaluation and scheduling optimization for a CPD project

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  • Xiaolei Wang
  • Tiejun Ci
  • Sang-Bing Tsai
  • Aijun Liu
  • Quan Chen

Abstract

In a collaborative product design project, reasonable resource allocation can shorten the development cycle and reduce cost. Team capacity evaluation and a task-team scheduling model are presented. A collaborative team capacity model is constructed, and a 2-tuple linguistic method is used to evaluate the capacity of collaborative teams. Next, the matching degree between design task and collaborative team is defined. A collaborative product design scheduling model considering task-team matching is developed. Combined with the simulated annealing operator, based on the single-coding strategy, self-adaptive multi-point cross and mutation, an improved genetic algorithm is proposed to solve the model. Finally, a case study is presented to validate the method.

Suggested Citation

  • Xiaolei Wang & Tiejun Ci & Sang-Bing Tsai & Aijun Liu & Quan Chen, 2018. "An empirical study of collaborative capacity evaluation and scheduling optimization for a CPD project," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-16, August.
    • Handle: RePEc:plo:pone00:0200753
    DOI: 10.1371/journal.pone.0200753
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    References listed on IDEAS

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    1. Chen, Shih-Pin & Tsai, Ming-Jiun, 2011. "Time-cost trade-off analysis of project networks in fuzzy environments," European Journal of Operational Research, Elsevier, vol. 212(2), pages 386-397, July.
    2. Sharron A. Frillman & Kari L. Wilde & Jonathan F. Kochert & Scott R. Homan & Cynthia L. Tomovic, 2010. "Entry-level engineering professionals and Product Lifecycle Management: a competency model," International Journal of Manufacturing Technology and Management, Inderscience Enterprises Ltd, vol. 19(3/4), pages 306-311.
    3. Herrera, F. & Martinez, L. & Sanchez, P. J., 2005. "Managing non-homogeneous information in group decision making," European Journal of Operational Research, Elsevier, vol. 166(1), pages 115-132, October.
    4. Hajara Idris & Absalom E Ezugwu & Sahalu B Junaidu & Aderemi O Adewumi, 2017. "An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-24, May.
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