IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i9p2619-d228821.html
   My bibliography  Save this article

Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing

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)

  • Jili Kong

    (School of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, China)

Abstract

Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2619-:d:228821
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/9/2619/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/9/2619/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    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. 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.
    5. Gilseung Ahn & You-Jin Park & Sun Hur, 2016. "The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing," Sustainability, MDPI, vol. 8(12), pages 1-17, November.
    6. Toly Chen, 2014. "Strengthening the Competitiveness and Sustainability of a Semiconductor Manufacturer with Cloud Manufacturing," Sustainability, MDPI, vol. 6(1), pages 1-16, January.
    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. Li-Lan Liu & Rong-Song Hu & Xiang-Ping Hu & Gai-Ping Zhao & Sen Wang, 2015. "A hybrid PSO-GA algorithm for job shop scheduling in machine tool production," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5755-5781, October.
    9. 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.
    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. 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.
    2. 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.

    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. 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.
    2. Daozhi Zhao & Yang Xue & Cejun Cao & Hongshuai Han, 2019. "Channel Selection and Pricing Decisions Considering Three Charging Modes of Production Capacity Sharing Platform: A Sustainable Operations Perspective," Sustainability, MDPI, vol. 11(21), pages 1-28, October.
    3. Yankai Wang & Shilong Wang & Bo Yang & Bo Gao & Sibao Wang, 2022. "An effective adaptive adjustment method for service composition exception handling in cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 735-751, March.
    4. Shuai Zhang & Yangbing Xu & Wenyu Zhang & Dejian Yu, 2019. "A new fuzzy QoS-aware manufacture service composition method using extended flower pollination algorithm," Journal of Intelligent Manufacturing, Springer, vol. 30(5), pages 2069-2083, June.
    5. Jiae Zhang & Jianjun Yang, 2016. "Flexible job-shop scheduling with flexible workdays, preemption, overlapping in operations and satisfaction criteria: an industrial application," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4894-4918, August.
    6. 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.
    7. Shalini Kumari & Sasadhar Bera, 2023. "Developing an emission risk control model in coal‐fired power plants for investigating CO2 reduction strategies for sustainable business development," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 842-857, January.
    8. Yan-chao Yin & Fu-zhao Chen & Wei-zhi Liao & Cui-yin Liu, 2019. "An Optimal Composition Strategy for Knowledge Service Component Based on Flexible Tracking Particle Swarm Algorithm," Complexity, Hindawi, vol. 2019, pages 1-14, December.
    9. Haibo Yi, 2021. "A post-quantum secure communication system for cloud manufacturing safety," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 679-688, March.
    10. Hao Li & Shanghua Mi & Qifeng Li & Xiaoyu Wen & Dongping Qiao & Guofu Luo, 2020. "A scheduling optimization method for maintenance, repair and operations service resources of complex products," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1673-1691, October.
    11. Akoz, Onur & Petrovic, Dobrila, 2007. "A fuzzy goal programming method with imprecise goal hierarchy," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1427-1433, September.
    12. Sharma, Dinesh K. & Jana, R.K., 2009. "A hybrid genetic algorithm model for transshipment management decisions," International Journal of Production Economics, Elsevier, vol. 122(2), pages 703-713, December.
    13. Ramtin Joolaie & Ahmad Abedi Sarvestani & Fatemeh Taheri & Steven Van Passel & Hossein Azadi, 2017. "Sustainable cropping pattern in North Iran: application of fuzzy goal programming," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(6), pages 2199-2216, December.
    14. Tin-Chih Toly Chen, 2017. "Cloud intelligence in manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1057-1059, June.
    15. K. Taghizadeh & M. Bagherpour & I. Mahdavi, 2011. "An interactive fuzzy goal programming approach for multi-period multi-product production planning problem," Fuzzy Information and Engineering, Springer, vol. 3(4), pages 393-410, December.
    16. Seyed Sina Mohri & Meisam Akbarzadeh, 2019. "Locating key stations of a metro network using bi-objective programming: discrete and continuous demand mode," Public Transport, Springer, vol. 11(2), pages 321-340, August.
    17. Shuangyao Zhao & Qiang Zhang & Zhanglin Peng & Xiaonong Lu, 2020. "Personalized manufacturing service composition recommendation: combining combinatorial optimization and collaborative filtering," Journal of Combinatorial Optimization, Springer, vol. 40(3), pages 733-756, October.
    18. Rifat G. Ozdemir & Ugur Cinar & Eren Kalem & Onur Ozcelik, 2016. "Sub-assembly detection and line balancing using fuzzy goal programming approach," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 8(1), pages 65-86.
    19. Toly Chen & Chi-Wei Lin, 2017. "Estimating the simulation workload for factory simulation as a cloud service," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1139-1157, June.
    20. R. Ghasemy Yaghin & S.M.T. Fatemi Ghomi & S.A. Torabi, 2015. "A hybrid credibility-based fuzzy multiple objective optimisation to differential pricing and inventory policies with arbitrage consideration," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(14), pages 2628-2639, October.

    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:gam:jsusta:v:11:y:2019:i:9:p:2619-:d:228821. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.