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Development of a new approach for deterministic supply chain network design

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  • Mohammadi Bidhandi, Hadi
  • Mohd. Yusuff, Rosnah
  • Megat Ahmad, Megat Mohamad Hamdan
  • Abu Bakar, Mohd Rizam

Abstract

This paper proposes a mixed integer linear programming model and solution algorithm for solving supply chain network design problems in deterministic, multi-commodity, single-period contexts. The strategic level of supply chain planning and tactical level planning of supply chain are aggregated to propose an integrated model. The model integrates location and capacity choices for suppliers, plants and warehouses selection, product range assignment and production flows. The open-or-close decisions for the facilities are binary decision variables and the production and transportation flow decisions are continuous decision variables. Consequently, this problem is a binary mixed integer linear programming problem. In this paper, a modified version of Benders' decomposition is proposed to solve the model. The most difficulty associated with the Benders' decomposition is the solution of master problem, as in many real-life problems the model will be NP-hard and very time consuming. In the proposed procedure, the master problem will be developed using the surrogate constraints. We show that the main constraints of the master problem can be replaced by the strongest surrogate constraint. The generated problem with the strongest surrogate constraint is a valid relaxation of the main problem. Furthermore, a near-optimal initial solution is generated for a reduction in the number of iterations.

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  • Mohammadi Bidhandi, Hadi & Mohd. Yusuff, Rosnah & Megat Ahmad, Megat Mohamad Hamdan & Abu Bakar, Mohd Rizam, 2009. "Development of a new approach for deterministic supply chain network design," European Journal of Operational Research, Elsevier, vol. 198(1), pages 121-128, October.
    • Handle: RePEc:eee:ejores:v:198:y:2009:i:1:p:121-128
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    3. Kiya, Farhad & Davoudpour, Hamid, 2012. "Stochastic programming approach to re-designing a warehouse network under uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(5), pages 919-936.
    4. Juan Yu & Mi Gan & Shaoquan Ni & Dingjun Chen, 2018. "Multi-objective models and real case study for dual-channel FAP supply chain network design with fuzzy information," Journal of Intelligent Manufacturing, Springer, vol. 29(2), pages 389-403, February.
    5. Jann Michael Weinand & Kenneth Sorensen & Pablo San Segundo & Max Kleinebrahm & Russell McKenna, 2020. "Research trends in combinatorial optimisation," Papers 2012.01294, arXiv.org.
    6. Nayeri, Sina & Sazvar, Zeinab & Heydari, Jafar, 2022. "A global-responsive supply chain considering sustainability and resiliency: Application in the medical devices industry," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    7. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    8. Sahling, Florian & Kayser, Ariane, 2016. "Strategic supply network planning with vendor selection under consideration of risk and demand uncertainty," Omega, Elsevier, vol. 59(PB), pages 201-214.
    9. Mariel, Katharina & Minner, Stefan, 2017. "Benders decomposition for a strategic network design problem under NAFTA local content requirements," Omega, Elsevier, vol. 68(C), pages 62-75.
    10. Longinidis, Pantelis & Georgiadis, Michael C., 2014. "Integration of sale and leaseback in the optimal design of supply chain networks," Omega, Elsevier, vol. 47(C), pages 73-89.
    11. Jahani, Hamed & Abbasi, Babak & Sheu, Jiuh-Biing & Klibi, Walid, 2024. "Supply chain network design with financial considerations: A comprehensive review," European Journal of Operational Research, Elsevier, vol. 312(3), pages 799-839.
    12. Alidaee, Bahram, 2014. "Zero duality gap in surrogate constraint optimization: A concise review of models," European Journal of Operational Research, Elsevier, vol. 232(2), pages 241-248.
    13. Matt Bassett & Leslie Gardner, 2013. "Designing optimal global supply chains at Dow AgroSciences," Annals of Operations Research, Springer, vol. 203(1), pages 187-216, March.
    14. Muhamad Deni Johansyah & Sundarapandian Vaidyanathan & Aceng Sambas & Khaled Benkouider & Seyed Mohammad Hamidzadeh & Monika Hidayanti, 2024. "Dynamical Analysis and Sliding Mode Controller for the New 4D Chaotic Supply Chain Model Based on the Product Received by the Customer," Mathematics, MDPI, vol. 12(13), pages 1-21, June.
    15. Sonia Mahjoub & Jean-Claude Hennet, 2014. "Manufacturers’ Coalition under a Price Elastic Market - a Quadratic Production Game Approach," Post-Print hal-01479690, HAL.
    16. Morteza Lalmazloumian & Kuan Yew Wong & Kannan Govindan & Devika Kannan, 2016. "A robust optimization model for agile and build-to-order supply chain planning under uncertainties," Annals of Operations Research, Springer, vol. 240(2), pages 435-470, May.
    17. Sina Abbasi & Ilias Vlachos & Ali Samadzadeh & Shayan Etemadifar & Mohamad Afshar & Mohsen Amra, 2024. "Modelling a Logistics and Financial Supply Chain Network during the COVID-19 Era," Logistics, MDPI, vol. 8(1), pages 1-21, March.
    18. Yu-Chung Tsao & Qinhong Zhang & Tsung-Hui Chen, 2016. "Multi-item distribution network design problems under volume discount on transportation cost," International Journal of Production Research, Taylor & Francis Journals, vol. 54(2), pages 426-443, January.

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