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Multi-objective bi-level supply chain network order allocation problem under fuzziness

Author

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  • Srikant Gupta

    (Aligarh Muslim University)

  • Irfan Ali

    (Aligarh Muslim University)

  • Aquil Ahmed

    (Aligarh Muslim University)

Abstract

In this paper we addressed supply chain network (SCN) as bi-level programming problem in which the primary objective is to determine optimal order allocation of products where the customer’s demands and supply for the products are fuzzy. In the proposed SCN model, we suppose that the first level (leader) and second level (follower) operate two separate groups of SCN. The leader, who moves first, determines quantities shipped to retailers, and then, the follower decides his quantities rationally. The leader’s objective is to minimize the total transportation costs, and similarly, the follower’s objective is to minimize the total delivery time of the SCN and at the same time balancing the optimal order allocation from each source, plant, retailer and warehouse respectively. The fuzzy goal programming approach has been used to achieve the highest degree of the membership goals by minimizing the deviational variables so that most satisfactory or the preferred solution for both the levels to be obtained. A numerical example is given to demonstrate the proposed methodology.

Suggested Citation

  • Srikant Gupta & Irfan Ali & Aquil Ahmed, 2018. "Multi-objective bi-level supply chain network order allocation problem under fuzziness," OPSEARCH, Springer;Operational Research Society of India, vol. 55(3), pages 721-748, November.
  • Handle: RePEc:spr:opsear:v:55:y:2018:i:3:d:10.1007_s12597-018-0340-2
    DOI: 10.1007/s12597-018-0340-2
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    References listed on IDEAS

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    1. Ahmad Yusuf Adhami & Syed Mohd Muneeb & Mohammad Asim Nomani, 2017. "A multilevel decision making model for the supplier selection problem in a fuzzy situation," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 27(4), pages 5-26.
    2. Zeng, Qing & Zhang, Baohua & Fang, Jiakun & Chen, Zhe, 2017. "A bi-level programming for multistage co-expansion planning of the integrated gas and electricity system," Applied Energy, Elsevier, vol. 200(C), pages 192-203.
    3. Lejeune, M.A., 2006. "A variable neighborhood decomposition search method for supply chain management planning problems," European Journal of Operational Research, Elsevier, vol. 175(2), pages 959-976, December.
    4. Liu, Shiang-Tai & Kao, Chiang, 2004. "Solving fuzzy transportation problems based on extension principle," European Journal of Operational Research, Elsevier, vol. 153(3), pages 661-674, March.
    5. Syed Aqib Jalil & Shakeel Javaid & Syed Mohd Muneeb, 2018. "A decentralized multi-level decision making model for solid transportation problem with uncertainty," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 9(5), pages 1022-1033, October.
    6. Chang, Yung-Chia & Lee, Chung-Yee, 2004. "Machine scheduling with job delivery coordination," European Journal of Operational Research, Elsevier, vol. 158(2), pages 470-487, October.
    7. Sakawa, Masatoshi & Nishizaki, Ichiro & Uemura, Yoshio, 2001. "Fuzzy programming and profit and cost allocation for a production and transportation problem," European Journal of Operational Research, Elsevier, vol. 131(1), pages 1-15, May.
    8. Selim, Hasan & Araz, Ceyhun & Ozkarahan, Irem, 2008. "Collaborative production-distribution planning in supply chain: A fuzzy goal programming approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(3), pages 396-419, May.
    9. Arindam Garai & Palash Mandal & Tapan Kumar Roy, 2016. "Intuitionistic fuzzy T-sets based optimization technique for production-distribution planning in supply chain management," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 950-975, December.
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    Cited by:

    1. Umar Muhammad Modibbo & Srikant Gupta & Aquil Ahmed & Irfan Ali, 2024. "An integrated multi-objective multi-product inventory managed production planning problem under uncertain environment," Annals of Operations Research, Springer, vol. 339(3), pages 1679-1723, August.

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