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Multi-objective-based differential evolution for balancing production cost, diversity and aggregated performance attributes in product family design

Author

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  • Ismail M. Ali

    (University of New South Wales)

  • Hasan H. Turan

    (University of New South Wales)

  • Ripon K. Chakrabortty

    (University of New South Wales)

  • Sondoss Elsawah

    (University of New South Wales)

Abstract

In product family design (PFD), deciding on a platform design strategy can be viewed as a multidisciplinary optimization problem that involves several factors, such as design variables, manufacturing costs, customizability, supplier reliability, and customer satisfaction. In this study, a multi-objective based differential evolution (MO-based DE) algorithm has been proposed for tackling the module-based PFD problem. The MO-based DE aims to find the best balance between many objectives, such as total production cost, diversity index, and a combination of other objectives (performance attributes). These objectives include commonality, modularity, and suppliers' reliability and all are aggregated to provide a goodness score. To effectively improve the DE's efficiency while solving such a complex optimization problem, the proposed DE integrates new elements such as (i) a novel solution representation, (ii) an improved heuristic technique for platform development, (iii) a weighted aggregation to combine different objectives, and (iv) a proposed platform-based crossover. To validate its performance, the proposed MO-based DE has been compared with (1) the standard DE to assess the effect of the incorporated new elements on DE’s performance, and (2) well-known fast non-dominant sorting genetic algorithms NSGA-II and (3) NSGA-III for solving a real case study of a family of kettles. The experimental results confirmed the efficacy of the proposed MO-based DE as follows: in terms of average cost value, MO-based DE outperformed standard DE and NSGA-II by 26.40% and 11.69%, respectively. While in terms of goodness score, it achieved 20.69% and 8.05% better scores compared to standard DE and NSGA-II, respectively. Moreover, the proposed MO-based DE attained a very competitive performance against NSGA-III as it reached a better average cost and goodness score of 1.74% and 0.82%, respectively.

Suggested Citation

  • Ismail M. Ali & Hasan H. Turan & Ripon K. Chakrabortty & Sondoss Elsawah, 2024. "Multi-objective-based differential evolution for balancing production cost, diversity and aggregated performance attributes in product family design," Flexible Services and Manufacturing Journal, Springer, vol. 36(1), pages 175-223, March.
    • Handle: RePEc:spr:flsman:v:36:y:2024:i:1:d:10.1007_s10696-022-09480-9
    DOI: 10.1007/s10696-022-09480-9
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    References listed on IDEAS

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    1. Rachel Campos Sabioni & Joanna Daaboul & Julien Le Duigou, 2022. "Concurrent optimisation of modular product and Reconfigurable Manufacturing System configuration: a customer-oriented offer for mass customisation," International Journal of Production Research, Taylor & Francis Journals, vol. 60(7), pages 2275-2291, April.
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    3. Francesco Gabriele Galizia & Hoda ElMaraghy & Marco Bortolini & Cristina Mora, 2020. "Product platforms design, selection and customisation in high-variety manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(3), pages 893-911, February.
    4. Mohmmad Hanafy & Hoda ElMaraghy, 2015. "Developing assembly line layout for delayed product differentiation using phylogenetic networks," International Journal of Production Research, Taylor & Francis Journals, vol. 53(9), pages 2633-2651, May.
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