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A Benders decomposition approach for a new sustainable pharmaceutical supply chain network: a case study in France

Author

Listed:
  • Fariba Goodarzian

    (University of Seville, Organization Engineering Group, School of Engineering)

  • Ajith Abraham

    (Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence)

  • Jesús Muñuzuri

    (University of Seville, Organization Engineering Group, School of Engineering)

  • Atour Taghipour

    (Normandy University, Faculty of International Business)

  • Peiman Ghasemi

    (University of Vienna, Department of Business Decisions and Analytics)

Abstract

Recently, sustainable supply chains have emerged to emphasize the importance of social and environmental concerns along with economic factors in supply chain management. In this context, there is a necessity for mathematical models that indicate environmental aspects and the social effects of the supply chain network. In the present study, a new multi-product production, distribution, and transportation, in which the economic, environmental, and social effects under CO2 emission to fill this gap are presented. Additionally, service technology under various criteria in the social aspects and time window for the earliest and latest arrival time of products to main distribution centers are provided. Also, other novelties of this paper, the environmental effects to open main and local distribution centers, warehouses, and pharmacies are considered as environmental effects. So, the main contributions of this paper are to develop a multi objective, dynamic production and distribution planning using time windows in service technology of sustainable pharmaceutical supply Chain Network. In this regard, the mathematical model is formulated as a mixed-integer linear programming model. Moreover, a Benders decomposition approach is appropriately extended to solve the presented model. In the proposed approach, the problem is decomposed into two models of sub-problem and a master. The master problem is developed by means of preprocessing and valid inequalities. The general and relative efficiency of the model and approach is experimentally assessed. The pharmaceutical production and distribution system of France is considered as a real case study in this paper. Eventually, the results indicate that the proposed approach considerably outperforms, and the efficiency of the developed model is verified through a set of sensitivity analyses.

Suggested Citation

  • Fariba Goodarzian & Ajith Abraham & Jesús Muñuzuri & Atour Taghipour & Peiman Ghasemi, 2026. "A Benders decomposition approach for a new sustainable pharmaceutical supply chain network: a case study in France," Annals of Operations Research, Springer, vol. 356(2), pages 881-919, January.
    • Handle: RePEc:spr:annopr:v:356:y:2026:i:2:d:10.1007_s10479-024-05936-3
    DOI: 10.1007/s10479-024-05936-3
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    References listed on IDEAS

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    1. Bahman Naderi & Kannan Govindan & Hamed Soleimani, 2020. "A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network," Annals of Operations Research, Springer, vol. 291(1), pages 685-705, August.
    2. Youssef Tliche & A. Taghipour & B. Canel-Depitre, 2019. "Downstream Demand Inference in decentralized supply chains," Post-Print hal-02173401, HAL.
    3. Tapia-Ubeda, Francisco J. & Miranda, Pablo A. & Macchi, Marco, 2018. "A Generalized Benders Decomposition based algorithm for an inventory location problem with stochastic inventory capacity constraints," European Journal of Operational Research, Elsevier, vol. 267(3), pages 806-817.
    4. Christina Arampantzi & Ioannis Minis & Georgios Dikas, 2019. "A strategic model for exact supply chain network design and its application to a global manufacturer," International Journal of Production Research, Taylor & Francis Journals, vol. 57(5), pages 1371-1397, March.
    5. Tliche, Y. & Taghipour, A. & Canel-Depitre, B., 2019. "Downstream Demand Inference in decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 274(1), pages 65-77.
    6. Sujeet Kumar Singh & Mark Goh, 2019. "Multi-objective mixed integer programming and an application in a pharmaceutical supply chain," International Journal of Production Research, Taylor & Francis Journals, vol. 57(4), pages 1214-1237, February.
    7. Tliche, Youssef & Taghipour, Atour & Canel-Depitre, Béatrice, 2020. "An improved forecasting approach to reduce inventory levels in decentralized supply chains," European Journal of Operational Research, Elsevier, vol. 287(2), pages 511-527.
    8. Mohammadali Vosooghidizaji & Atour Taghipour & Béatrice Canel-Depitre, 2020. "Supply chain coordination under information asymmetry: a review," International Journal of Production Research, Taylor & Francis Journals, vol. 58(6), pages 1805-1834, March.
    9. Zahiri, Behzad & Zhuang, Jun & Mohammadi, Mehrdad, 2017. "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 109-142.
    10. Abbas Ahmadi & Mohammad Mousazadeh & S. Ali Torabi & Mir Saman Pishvaee, 2018. "OR Applications in Pharmaceutical Supply Chain Management," International Series in Operations Research & Management Science, in: Cengiz Kahraman & Y. Ilker Topcu (ed.), Operations Research Applications in Health Care Management, chapter 0, pages 461-491, Springer.
    11. Rahmaniani, Ragheb & Crainic, Teodor Gabriel & Gendreau, Michel & Rei, Walter, 2017. "The Benders decomposition algorithm: A literature review," European Journal of Operational Research, Elsevier, vol. 259(3), pages 801-817.
    12. Chung, Sung Hoon & Kwon, Changhyun, 2016. "Integrated supply chain management for perishable products: Dynamics and oligopolistic competition perspectives with application to pharmaceuticals," International Journal of Production Economics, Elsevier, vol. 179(C), pages 117-129.
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