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A New Version of African Vulture Optimizer for Apparel Supply Chain Management Based on Reorder Decision-Making

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  • Shayan Bahadoran Baghbadorani

    (Department of Industrial Engineering & Management, Shanghai Jiao Tong University (SJTU), Shanghai 200240, China)

  • Seyed Abdolhassan Johari

    (Management Faculty, Tehran University, Tehran 1417935840, Iran)

  • Zahra Fakhri

    (Department of Marketing, Haslam College of Business, University of Tennessee, Knoxville, TN 37916, USA)

  • Esmaeil Khaksar Shahmirzadi

    (Faculty of Tourism, Near East University (NEU), Nicosia 99138, Turkish Republic of North Cyprus, Turkey)

  • Shavkatov Navruzbek Shavkatovich

    (The Department of Corporate Finance and Securities, Tashkent Institute of Finance, Tashkent 100000, Uzbekistan)

  • Sangkeum Lee

    (Environment ICT Research Section, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea)

Abstract

Supply chains may serve as an effective platform for the development of sustainability by encouraging responsible conduct throughout all chain members and stages. Agent technology may greatly aid in decision-making during supply chain management. Due to recent changes in the seasons, fashion trends, and the requirements of various religions, particularly with regard to the ordering procedure, the supply chain for clothing has become one of the most difficult duties in this area. Because of this, it is crucial to enhance process coordination throughout the whole clothing supply chain and develop a decision-making strategy that functions best in a fluid environment. The Unified Modeling Language (UML) is used in this work to define the relationship between agents and simulate the supply chain process. This research incorporates enhanced African vulture optimizer, a modified bio-inspired approach, and fuzzy inference theory to assist the supply chain agent in determining the appropriate replenishment quantity and reorder point to lower the inventory cost. According to test results, the suggested AAVO-based technique may be successful in determining a target demand ordering amount while reducing the overall cost of the supply chain due to a lowered convergence trend and algorithm accuracy.

Suggested Citation

  • Shayan Bahadoran Baghbadorani & Seyed Abdolhassan Johari & Zahra Fakhri & Esmaeil Khaksar Shahmirzadi & Shavkatov Navruzbek Shavkatovich & Sangkeum Lee, 2022. "A New Version of African Vulture Optimizer for Apparel Supply Chain Management Based on Reorder Decision-Making," Sustainability, MDPI, vol. 15(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:400-:d:1015868
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    References listed on IDEAS

    as
    1. Choi, Tsan-Ming & Chen, Yue, 2021. "Circular supply chain management with large scale group decision making in the big data era: The macro-micro model," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Ke Ma & Lichuan Wang & Yan Chen, 2017. "A Collaborative Cloud Service Platform for Realizing Sustainable Make-To-Order Apparel Supply Chain," Sustainability, MDPI, vol. 10(1), pages 1-21, December.
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