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Module-Based Product Configuration Decisions Considering Both Economical and Carbon Emission-Related Environmental Factors

Author

Listed:
  • Dong Yang

    (School of Business and Management, Donghua University, Shanghai 200051, China)

  • Jia Li

    (School of Business and Management, Donghua University, Shanghai 200051, China)

  • Bill Wang

    (Business Information Systems Department, Auckland University of Technology, Auckland 1010, New Zealand)

  • Yong-ji Jia

    (School of Business and Management, Donghua University, Shanghai 200051, China)

Abstract

With an increasing demand for green and environmentally friendly products, configuring a customized product from modular product design to satisfy both individualized customer requirements and environment restrictions has become a vital task for mass customization. In this paper, we addressed a module-based product configuration problem under carbon emission regulation, i.e., carbon cap-and-trade schema. The configuration problem under carbon cap-and-trade schema was formulated as a mixed-integer programming problem. Due to the NP-hard characteristics of the model, a genetic algorithm was employed to solve the configuration problems under carbon cap-and-trade. Experiments were carried out to analyze the effects of carbon emissions on product configuration decisions and carbon purchasing decisions. As a result, the range for carbon emission cap is suggested for both manufacturers and government agency, which provides the decision supporting for both customized and low-carbon production and green manufacturing.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:3:p:1145-:d:316962
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    References listed on IDEAS

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    2. Yao Jiao & Yu Yang, 2019. "A product configuration approach based on online data," Journal of Intelligent Manufacturing, Springer, vol. 30(6), pages 2473-2487, August.
    3. Amina Chelly & Imen Nouira & Yannick Frein & Atidel Hadj-Alouane, 2019. "On The consideration of carbon emissions in modelling-based supply chain literature: the state of the art, relevant features and research gaps," Post-Print hal-02285426, HAL.
    4. Amina Chelly & Imen Nouira & Yannick Frein & Atidel B. Hadj-Alouane, 2019. "On The consideration of carbon emissions in modelling-based supply chain literature: the state of the art, relevant features and research gaps," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4977-5004, August.
    5. Carl, Jeremy & Fedor, David, 2016. "Tracking global carbon revenues: A survey of carbon taxes versus cap-and-trade in the real world," Energy Policy, Elsevier, vol. 96(C), pages 50-77.
    6. Dunbing Tang & Qi Wang & Inayat Ullah, 2017. "Optimisation of product configuration in consideration of customer satisfaction and low carbon," International Journal of Production Research, Taylor & Francis Journals, vol. 55(12), pages 3349-3373, June.
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    Cited by:

    1. 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.
    2. Rohit Agrawal & Abhijit Majumdar & Anil Kumar & Sunil Luthra, 2023. "Integration of artificial intelligence in sustainable manufacturing: current status and future opportunities," Operations Management Research, Springer, vol. 16(4), pages 1720-1741, December.
    3. 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.

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