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Coordinated optimisation of platform-driven product line planning by bilevel programming

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  • Chenlu Miao
  • Gang Du
  • Roger J. Jiao
  • Tiebin Zhang

Abstract

Product line planning (PLP) aims at an optimal combination of product feature offerings, suggesting itself to be a determinant decision for a company to satisfy diverse customer needs and gain competitive advantages. Fulfilment of planned product lines must make trade-offs between product variety and production costs. To balance the costs of product lines, manufacturers often adopt a product platform configuration (PPC) approach to redesign product and process platforms by adding new modules to the legacy platforms. The PPC is an effective means of providing product variety while controlling the manufacturing costs. The PLP and PPC problems have traditionally been investigated separately in the marketing research and engineering design fields. It is important to coordinate PLP and PPC decisions within a coherent optimisation framework. This paper proposes a bilevel mixed 0–1 nonlinear programming model to formulate coordinated optimisation for platform-driven product line planning. The upper level deals with the PLP problem by maximising the profit of an entire product line, whilst the lower level copes with the multiple product platforms optimisation for the optimal PPC in accordance with the upper level decisions of product line structure. To solve this bilevel programming model, a bilevel genetic algorithm is developed to find the optimal solution. A case study of coordinated optimisation between an automobile line and its product platforms is presented to demonstrate the feasibility and effectiveness of the proposed bilevel programming in comparison with a typical ‘all-in-one’ approach and a non-joint optimisation programming.

Suggested Citation

  • Chenlu Miao & Gang Du & Roger J. Jiao & Tiebin Zhang, 2017. "Coordinated optimisation of platform-driven product line planning by bilevel programming," International Journal of Production Research, Taylor & Francis Journals, vol. 55(13), pages 3808-3831, July.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:13:p:3808-3831
    DOI: 10.1080/00207543.2017.1294770
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    References listed on IDEAS

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    1. Jerome Bracken & James T. McGill, 1973. "Mathematical Programs with Optimization Problems in the Constraints," Operations Research, INFORMS, vol. 21(1), pages 37-44, February.
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    Cited by:

    1. 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).
    2. 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).
    3. 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.
    4. Jiang, Lijun & Wang, Xifu & Yang, Kai & Gao, Yiwen, 2023. "Bilevel optimization for the reorganization of inland river ports: A niche perspective," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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