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Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming

Author

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  • Xiaojie Liu

    (School of Management, Tianjin University of Commerce, Tianjin 300134, China
    Research Center for Management Innovation and Evaluation, Tianjin University of Commerce, Tianjin 300134, China)

  • Xuejian Gong

    (The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA)

  • Roger J. Jiao

    (The George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0405, USA)

Abstract

The conversion of manufacturing functional areas towards services implies a paradigm of Manufacturing as a Service (MaaS). It transforms the product fulfillment process to a distributed one via a service-oriented manufacturing platform. Successful MaaS operational planning must be coordinated with low-carbon product family planning (PFP) at the front end of product design and development. These changes challenge the traditional PFP design, considering its manufacturer loading balancing (MLB) problem, which is limited to integrated product fulfillment. This paper proposes a leader–follower interactive decision-making mechanism for distributed collaborative product fulfillment of low-carbon PFP and MLB based on a Stackelberg game. A bilevel optimization model with linear physical programming was developed and solved, comprising an upper-level PFP optimization problem and a lower-level MLB optimization problem. The upper-level PFP aims to determine the optimal configuration of each product variant with the objective of maximizing the market share and the total profit in the product family. The lower-level MLB seeks for the optimal partition of manufacturing processes among manufacturers in order to minimize the low-carbon operation cost of product variants and balance the loads among manufacturers. A case study of WS custom kitchen product family design for MaaS is reported to demonstrate the feasibility and potential of the proposed bilevel interactive optimization approach.

Suggested Citation

  • Xiaojie Liu & Xuejian Gong & Roger J. Jiao, 2022. "Low-Carbon Product Family Planning for Manufacturing as a Service (MaaS): Bilevel Optimization with Linear Physical Programming," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12566-:d:932229
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    References listed on IDEAS

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    1. 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.
    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.
    3. Dhingra, A. K. & Rao, S. S., 1995. "A cooperative fuzzy game theoretic approach to multiple objective design optimization," European Journal of Operational Research, Elsevier, vol. 83(3), pages 547-567, June.
    4. Dong Yang & Jia Li & Bill Wang & Yong-ji Jia, 2020. "Module-Based Product Configuration Decisions Considering Both Economical and Carbon Emission-Related Environmental Factors," Sustainability, MDPI, vol. 12(3), pages 1-13, February.
    5. Chaudhuri, Atanu & Datta, Partha Priya & Fernandes, Kiran J. & Xiong, Yu, 2021. "Optimal pricing strategies for manufacturing-as-a service platforms to ensure business sustainability," International Journal of Production Economics, Elsevier, vol. 234(C).
    6. Mehmet Ali Ilgin & Hakan Akçay & Ceyhun Araz, 2017. "Disassembly line balancing using linear physical programming," International Journal of Production Research, Taylor & Francis Journals, vol. 55(20), pages 6108-6119, October.
    7. Andrew Kusiak, 2020. "Service manufacturing = Process-as-a-Service + Manufacturing Operations-as-a-Service," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 1-2, January.
    8. Abbas, Mohamed & ElMaraghy, Hoda, 2018. "Co-platforming of products and assembly systems," Omega, Elsevier, vol. 78(C), pages 5-20.
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    1. Helo, Petri & Mayanti, Bening & Bejarano, Ronal & Sundman, Christian, 2024. "Sustainable supply chains – Managing environmental impact data on product platforms," International Journal of Production Economics, Elsevier, vol. 270(C).

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