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Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities

Author

Listed:
  • Wei He

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Guozhu Jia

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Hengshan Zong

    (Institute of systems engineering, China Aerospace Academy of Systems Science and Engineering, Beijing 100048, China)

  • Tao Huang

    (Center for Industrial Production, Aalborg University, Aalborg, Fibigerstraede 16, DK-9220 Aalborg, Denmark)

Abstract

In recent years, with the support of new information technology and national policies, cloud manufacturing (CMfg) has developed rapidly in China. About CMfg, scholars have conducted extensive and in-depth research, among which multi-objective service selection and scheduling (SSS) attracts increasing attention. Generally, the objectives of the SSS problem involve several aspects, such as time, cost, environment and quality. In order to select an optimal solution, the preference of a decision maker (DM) becomes key information. As one kind of typical preference information, objective priorities are less considered in current studies. So, in this paper, a multi-objective model is first constructed for the SSS with different objective priorities. Then, a two-phase method based on the order of priority satisfaction (TP-OPS) is designed to solve this problem. Finally, computational experiments are conducted for problems with different services and tasks/subtasks, as well as different preference information. The results show that the proposed TP-OPS method can achieve a balance between the maximum comprehensive satisfaction and satisfaction differences, which is conducive to the sustainable development of CMfg. In addition, the proposed method allows the preference information to be gradually clarified, which has the advantage of providing convenience to DM.

Suggested Citation

  • Wei He & Guozhu Jia & Hengshan Zong & Tao Huang, 2019. "Multi-Objective Cloud Manufacturing Service Selection and Scheduling with Different Objective Priorities," Sustainability, MDPI, vol. 11(17), pages 1-24, September.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:17:p:4767-:d:262850
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    References listed on IDEAS

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    1. Jiajun Zhou & Xifan Yao, 2017. "A hybrid approach combining modified artificial bee colony and cuckoo search algorithms for multi-objective cloud manufacturing service composition," International Journal of Production Research, Taylor & Francis Journals, vol. 55(16), pages 4765-4784, August.
    2. Gang Liu & Lu Shi & Kevin W. Li, 2018. "Equitable Allocation of Blue and Green Water Footprints Based on Land-Use Types: A Case Study of the Yangtze River Economic Belt," Sustainability, MDPI, vol. 10(10), pages 1-27, October.
    3. Chen, Jian & Huang, George Q. & Wang, Jun-Qiang & Yang, Chen, 2019. "A cooperative approach to service booking and scheduling in cloud manufacturing," European Journal of Operational Research, Elsevier, vol. 273(3), pages 861-873.
    4. 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.
    5. Tao, Fei & Zhao, Dongming & Yefa, Hu & Zhou, Zude, 2010. "Correlation-aware resource service composition and optimal-selection in manufacturing grid," European Journal of Operational Research, Elsevier, vol. 201(1), pages 129-143, February.
    6. Chen, Liang-Hsuan & Tsai, Feng-Chou, 2001. "Fuzzy goal programming with different importance and priorities," European Journal of Operational Research, Elsevier, vol. 133(3), pages 548-556, September.
    7. Feng Xiang & Yefa Hu & Yingrong Yu & Huachun Wu, 2014. "QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 22(4), pages 663-685, December.
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