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Joint optimization of product family configuration and scaling design by Stackelberg game

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  • Du, Gang
  • Jiao, Roger J.
  • Chen, Mo

Abstract

Product family design is generally characterized by two types of approaches: module-based and scale-based. While the former aims to enable product variety based on module configuration, the latter is to variegate product design by scaling up or down certain design parameters. The prevailing practice is to treat module configuration and scaling design as separate decisions or aggregate two design problems as a single-level, all-in-one optimization problem. In practice, optimization of scaling variables is always enacted within a specific modular platform; and meanwhile an optimal module configuration depends on how design parameters are to be scaled. The key challenge is how to deal with explicitly the coupling of these two design optimization problems.

Suggested Citation

  • Du, Gang & Jiao, Roger J. & Chen, Mo, 2014. "Joint optimization of product family configuration and scaling design by Stackelberg game," European Journal of Operational Research, Elsevier, vol. 232(2), pages 330-341.
  • Handle: RePEc:eee:ejores:v:232:y:2014:i:2:p:330-341
    DOI: 10.1016/j.ejor.2013.07.021
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    References listed on IDEAS

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    1. Yu, Yugang & Huang, George Q., 2010. "Nash game model for optimizing market strategies, configuration of platform products in a Vendor Managed Inventory (VMI) supply chain for a product family," European Journal of Operational Research, Elsevier, vol. 206(2), pages 361-373, October.
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    1. Van den Broeke, Maud & Boute, Robert & Cardoen, Brecht & Samii, Behzad, 2017. "An efficient solution method to design the cost-minimizing platform portfolio," European Journal of Operational Research, Elsevier, vol. 259(1), pages 236-250.
    2. Helene Krieg & Tobias Seidel & Jan Schwientek & Karl-Heinz Küfer, 2022. "Solving continuous set covering problems by means of semi-infinite optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 96(1), pages 39-82, August.
    3. Jiawen Hu & Zuhua Jiang & Hong Wang, 2017. "Joint Optimization of Production Plan and Preventive Maintenance Schedule by Stackelberg Game," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 34(04), pages 1-28, August.
    4. Ming Zhang & Jianjun Zhu & Hehua Wang & Pei Liu, 2019. "Evolutionary Game Analysis on Strategies in “Main Manufacturer–Supplier” Mode Considering Technology Docking and Price Concluding under Competition Condition," Mathematics, MDPI, vol. 7(12), pages 1-25, December.
    5. Ma, Yujie & Du, Gang & Jiao, Roger J., 2020. "Optimal crowdsourcing contracting for reconfigurable process planning in open manufacturing: A bilevel coordinated optimization approach," International Journal of Production Economics, Elsevier, vol. 228(C).
    6. Richárd Kicsiny, 2017. "Solution for a class of closed-loop leader-follower games with convexity conditions on the payoffs," Annals of Operations Research, Springer, vol. 253(1), pages 405-429, June.
    7. Gauss, Leandro & Lacerda, Daniel P. & Cauchick Miguel, Paulo A., 2022. "Market-Driven Modularity: Design method developed under a Design Science paradigm," International Journal of Production Economics, Elsevier, vol. 246(C).
    8. Yu Guodong & Yang Yu & Zhang Xuefeng & Li Chi, 2017. "Network-Based Analysis of Requirement Change in Customized Complex Product Development," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(04), pages 1125-1149, July.
    9. Kicsiny, R. & Varga, Z. & Scarelli, A., 2014. "Backward induction algorithm for a class of closed-loop Stackelberg games," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1021-1036.
    10. Robert N. Boute & Maud M. Van den Broeke & Kristof A. Deneire, 2017. "Barco Implements Platform-Based Product Development in Its Healthcare Division," Decision Analysis, INFORMS, vol. 48(01), pages 35-44, February.
    11. Leandro Gauss & Daniel P. Lacerda & Paulo A. Cauchick Miguel, 2021. "Module-based product family design: systematic literature review and meta-synthesis," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 265-312, January.
    12. Xiong, Yixuan & Du, Gang & Jiao, Roger J., 2018. "Modular product platforming with supply chain postponement decisions by leader-follower interactive optimization," International Journal of Production Economics, Elsevier, vol. 205(C), pages 272-286.
    13. Gang Du & Yi Xia & Roger J. Jiao & Xiaojie Liu, 2019. "Leader-follower joint optimization problems in product family design," Journal of Intelligent Manufacturing, Springer, vol. 30(3), pages 1387-1405, March.
    14. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    15. Samyeon Kim & Seung Ki Moon, 2019. "Eco-modular product architecture identification and assessment for product recovery," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 383-403, January.
    16. Wu, Jun & Du, Gang & Jiao, Roger J., 2021. "Optimal postponement contracting decisions in crowdsourced manufacturing: A three-level game-theoretic model for product family architecting considering subcontracting," European Journal of Operational Research, Elsevier, vol. 291(2), pages 722-737.
    17. Heradio, Ruben & Perez-Morago, Hector & Alférez, Mauricio & Fernandez-Amoros, David & Alférez, Germán H., 2016. "Augmenting measure sensitivity to detect essential, dispensable and highly incompatible features in mass customization," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1066-1077.

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