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A Unique Bifuzzy Manufacturing Service Composition Model Using an Extended Teaching-Learning-Based Optimization Algorithm

Author

Listed:
  • Yushu Yang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jie Lin

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Zijuan Hu

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

In today’s competitive and rapidly evolving manufacturing environment, optimizing the composition of manufacturing services is critical for effective supply chain deployment. Since the manufacturing environment involves many two-fold uncertainties, there are limited studies that have specifically tackled these two-fold uncertainties. Based on bifuzzy theory, we put forward a unique bifuzzy manufacturing service portfolio model. Through the application of the fuzzy variable to express quality of service (QoS) value of manufacturing services, this model also accounts for the preferences of manufacturing firms by allocating various weights to different sub-tasks. Next, we address the multi-objective optimization issue through the application of extended teaching-learning-based optimization (ETLBO) algorithm. The improvements of the ETLBO algorithm include utilizing the adaptive parameters and introducing a local search strategy combined with a genetic algorithm (GA). Finally, we conduct simulation experiments to show off the efficacy and efficiency of the suggested approach in comparison to six other benchmark algorithms.

Suggested Citation

  • Yushu Yang & Jie Lin & Zijuan Hu, 2024. "A Unique Bifuzzy Manufacturing Service Composition Model Using an Extended Teaching-Learning-Based Optimization Algorithm," Mathematics, MDPI, vol. 12(18), pages 1-26, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2947-:d:1483125
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    References listed on IDEAS

    as
    1. Wenyu Zhang & Yushu Yang & Shuai Zhang & Dejian Yu & Yangbing Xu, 2016. "A New Manufacturing Service Selection and Composition Method Using Improved Flower Pollination Algorithm," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-12, December.
    2. Fei Wang & Yuanjun Laili & Lin Zhang, 2021. "A many-objective memetic algorithm for correlation-aware service composition in cloud manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(17), pages 5179-5197, September.
    3. Wenyu Zhang & Shuai Zhang & Shanshan Guo & Yushu Yang & Yong Chen, 2017. "Concurrent optimal allocation of distributed manufacturing resources using extended Teaching-Learning-Based Optimization," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 718-735, February.
    4. Jiuping Xu & Jun Gang & Xiao Lei, 2013. "Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-22, 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. Wenyu Zhang & Yushu Yang & Shuai Zhang & Dejian Yu & Yacheng Li, 2018. "Correlation-aware manufacturing service composition model using an extended flower pollination algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 56(14), pages 4676-4691, July.
    Full references (including those not matched with items on IDEAS)

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