IDEAS home Printed from https://ideas.repec.org/a/taf/tprsxx/v55y2017i20p5916-5941.html
   My bibliography  Save this article

Bilevel joint optimisation for product family architecting considering make-or-buy decisions

Author

Listed:
  • Xiaojie Liu
  • Gang Du
  • Roger J. Jiao

Abstract

Product family architecting (PFA) aims at identification of common modules and selective modules to enable product family configuration for mass customisation. Due to nowadays manufacturers moving more towards assembly-to-order production throughout a distributed supply chain, the common practice of outsourcing of certain modules entails make-or-buy (MOB) decisions that must be taken into account in PFA. While the PFA and MOB decisions are enacted for different concerns of the manufacturer and the suppliers, it is important to deal with joint optimisation of the PFA and MOB problems. The prevailing decision models for joint optimisation are mainly originated from an ‘all-in-one’ approach that assumes both PFA and MOB decisions can be integrated into one single-level optimisation problem. Such an assumption neglects the complex trade-offs underlying two different decision-making problems and fails to reveal the inherent coupling of PFA and MOB decisions. This paper proposes to formulate joint optimisation of the PFA and MOB problems as a Stackelberg game, in which a bilevel decision mechanism model is deployed to reveal the inherent coupling and hierarchical relationships between PFA and MOB decisions. A nonlinear bilevel optimisation model is developed with the PFA problem acting as the leader and each MOB problem performing as a follower. A nested genetic algorithm is developed to solve the bilevel optimisation model. A case study of power transformer PFA subject to MOB considerations is presented to illustrate the feasibility and effectiveness of bilevel joint optimisation.

Suggested Citation

  • Xiaojie Liu & Gang Du & Roger J. Jiao, 2017. "Bilevel joint optimisation for product family architecting considering make-or-buy decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 5916-5941, October.
  • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:20:p:5916-5941
    DOI: 10.1080/00207543.2017.1304666
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207543.2017.1304666
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/00207543.2017.1304666?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Winfried Steiner & Harald Hruschka, 2002. "A Probabilistic One-Step Approach to the Optimal Product Line Design Problem Using Conjoint and Cost Data," Review of Marketing Science Working Papers 1-4-1003, Berkeley Electronic Press.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Andrade, Xavier & Guimarães, Luís & Figueira, Gonçalo, 2021. "Product line selection of fast-moving consumer goods," Omega, Elsevier, vol. 102(C).
    3. Tsafarakis, Stelios & Marinakis, Yannis & Matsatsinis, Nikolaos, 2011. "Particle swarm optimization for optimal product line design," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 13-22.
    4. Schön, Cornelia, 2010. "On the product line selection problem under attraction choice models of consumer behavior," European Journal of Operational Research, Elsevier, vol. 206(1), pages 260-264, October.
    5. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    6. Cornelia Schön, 2010. "On the Optimal Product Line Selection Problem with Price Discrimination," Management Science, INFORMS, vol. 56(5), pages 896-902, May.
    7. Guangyu Zou & Zhongkai Li & Chao He, 2023. "A New Product Configuration Model for Low Product Cost and Carbon-Neutral Expenditure," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    8. Xiaojie Liu & Gang Du & Roger J. Jiao & Yi Xia, 2017. "Product line design considering competition by bilevel optimization of a Stackelberg–Nash game," IISE Transactions, Taylor & Francis Journals, vol. 49(8), pages 768-780, August.
    9. S Tsafarakis & E Grigoroudis & N Matsatsinis, 2011. "Consumer choice behaviour and new product development: an integrated market simulation approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(7), pages 1253-1267, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tprsxx:v:55:y:2017:i:20:p:5916-5941. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TPRS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.