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Mining Pareto-optimal modules for delayed product differentiation

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  • Song, Zhe
  • Kusiak, Andrew

Abstract

This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost.

Suggested Citation

  • Song, Zhe & Kusiak, Andrew, 2010. "Mining Pareto-optimal modules for delayed product differentiation," European Journal of Operational Research, Elsevier, vol. 201(1), pages 123-128, February.
  • Handle: RePEc:eee:ejores:v:201:y:2010:i:1:p:123-128
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    References listed on IDEAS

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    Cited by:

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    2. Qian, Yanjun & Xie, Min & Goh, Thong Ngee & Lin, Jun, 2010. "Optimal testing strategies in overlapped design process," European Journal of Operational Research, Elsevier, vol. 206(1), pages 131-143, October.
    3. Afshin Mansouri, S. & Gallear, David & Askariazad, Mohammad H., 2012. "Decision support for build-to-order supply chain management through multiobjective optimization," International Journal of Production Economics, Elsevier, vol. 135(1), pages 24-36.
    4. Fabricio Eidelwein & Fabio Antonio Sartori Piran & Daniel Pacheco Lacerda & Aline Dresch & Luis Henrique Rodrigues, 2018. "Exploratory Analysis of Modularization Strategy Based on the Theory of Constraints Thinking Process," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 19(2), pages 111-122, June.
    5. Hong-Sen Yan & Wen-Chao Li, 2017. "A multi-objective scheduling algorithm with self-evolutionary feature for job-shop-like knowledgeable manufacturing cell," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 337-351, February.
    6. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.

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