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Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability

Author

Listed:
  • Dong Yang

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

  • Qidong Liu

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

  • Jia Li

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

  • Yongji Jia

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

Abstract

Cloud manufacturing is an emerging service-oriented paradigm that works by taking advantage of distributed manufacturing resources and capabilities to collaboratively perform a manufacturing task, with the consideration of QoS (Quality of Service) requirements such as cost, time and quality. Incorporating environmental concerns and sustainability into cloud manufacturing to produce a much greener product has become an urgent issue since there is fierce market competition and an increasing environment consciousness from customers. In this paper, we present a multi-objective optimization approach to selecting and scheduling cloud manufacturing services from the viewpoints of the economy and environment including carbon emissions and water resource. Subject to the carbon cap regulation, a multi-objective model for a cloud manufacturing task is built with the aim of minimizing total costs, carbon emissions, and water resource use. Transportation mode selections and carbon emissions from both cloud manufacturing services and transportation activities are taken into account in this model. The ε-constraint method is employed to obtain the exact Pareto front of optimal solutions. A case study from automobile cloud manufacturing is used to illustrate the effectiveness of the presented approach. Numerical experiments are conducted to compare the presented approach and the simple additive weighting method. The results show that the presented ε -constraint method can obtain a better and more diverse Pareto set of solutions and that it can solve the models in a reasonable time.

Suggested Citation

  • Dong Yang & Qidong Liu & Jia Li & Yongji Jia, 2020. "Multi-Objective Optimization of Service Selection and Scheduling in Cloud Manufacturing Considering Environmental Sustainability," Sustainability, MDPI, vol. 12(18), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7733-:d:415596
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    References listed on IDEAS

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    1. Wei He & Guozhu Jia & Hengshan Zong & Jili Kong, 2019. "Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing," Sustainability, MDPI, vol. 11(9), pages 1-15, May.
    2. Feng Li & Lin Zhang & T. W. Liao & Yongkui Liu, 2019. "Multi-objective optimisation of multi-task scheduling in cloud manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3847-3863, June.
    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. Yingfeng Zhang & Dong Xi & Haidong Yang & Fei Tao & Zhe Wang, 2019. "Cloud manufacturing based service encapsulation and optimal configuration method for injection molding machine," Journal of Intelligent Manufacturing, Springer, vol. 30(7), pages 2681-2699, October.
    6. Yongkui Liu & Lihui Wang & Xi Vincent Wang & Xun Xu & Lin Zhang, 2019. "Scheduling in cloud manufacturing: state-of-the-art and research challenges," International Journal of Production Research, Taylor & Francis Journals, vol. 57(15-16), pages 4854-4879, August.
    7. Jorick Lartigau & Xiaofei Xu & Lanshun Nie & Dechen Zhan, 2015. "Cloud manufacturing service composition based on QoS with geo-perspective transportation using an improved Artificial Bee Colony optimisation algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 53(14), pages 4380-4404, July.
    8. 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.
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