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Investigating replenishment policies for centralised and decentralised supply chains using stochastic programming approach

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  • M. Fattahi
  • M. Mahootchi
  • S.M. Moattar Husseini
  • E. Keyvanshokooh
  • F. Alborzi

Abstract

In this paper, a multiple period replenishment problem based on (s, S) policy is investigated for a supply chain (SC) comprising one retailer and one manufacturer with uncertain demand. Novel mixed-integer linear programming (MILP) models are developed for centralised and decentralised decision-making modes using two-stage stochastic programming. To compare these decision-making modes, a Monte Carlo simulation is applied to the optimization models’ policies. To deal with demand uncertainty, scenarios are generated using Latin Hypercube Sampling method and their number is reduced by a scenario reduction technique. In large test problems, where CPLEX solver is not able to reach an optimal solution in the centralised model, evolutionary strategies (ES) and imperialist competitive algorithm (ICA) are applied to find near optimal solutions. Sensitivity analysis is conducted to show the performance of the proposed mathematical models. Moreover, it is demonstrated that both ES and ICA provide acceptable solutions compared to the exact solutions of the MILP model. Finally, the main parameters affecting difference between profits of centralised and decentralised SCs are investigated using the simulation method.

Suggested Citation

  • M. Fattahi & M. Mahootchi & S.M. Moattar Husseini & E. Keyvanshokooh & F. Alborzi, 2015. "Investigating replenishment policies for centralised and decentralised supply chains using stochastic programming approach," International Journal of Production Research, Taylor & Francis Journals, vol. 53(1), pages 41-69, January.
    • Handle: RePEc:taf:tprsxx:v:53:y:2015:i:1:p:41-69
    DOI: 10.1080/00207543.2014.922710
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    Cited by:

    1. de Kok, Ton & Grob, Christopher & Laumanns, Marco & Minner, Stefan & Rambau, Jörg & Schade, Konrad, 2018. "A typology and literature review on stochastic multi-echelon inventory models," European Journal of Operational Research, Elsevier, vol. 269(3), pages 955-983.
    2. Govindan, Kannan & Fattahi, Mohammad, 2017. "Investigating risk and robustness measures for supply chain network design under demand uncertainty: A case study of glass supply chain," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 680-699.
    3. Zhu, Wenge & He, Yuanjie, 2017. "Green product design in supply chains under competition," European Journal of Operational Research, Elsevier, vol. 258(1), pages 165-180.
    4. Visentin, Andrea & Prestwich, Steven & Rossi, Roberto & Tarim, S. Armagan, 2021. "Computing optimal (R,s,S) policy parameters by a hybrid of branch-and-bound and stochastic dynamic programming," European Journal of Operational Research, Elsevier, vol. 294(1), pages 91-99.
    5. Bo Dai & Fenfen Li, 2021. "Joint Inventory Replenishment Planning of an E-Commerce Distribution System with Distribution Centers at Producers’ Locations," Logistics, MDPI, vol. 5(3), pages 1-14, July.
    6. Dehghani, Maryam & Abbasi, Babak & Oliveira, Fabricio, 2021. "Proactive transshipment in the blood supply chain: A stochastic programming approach," Omega, Elsevier, vol. 98(C).
    7. Attari, Mahdi Yousefi Nejad & Torkayesh, Ali Ebadi, 2018. "Developing benders decomposition algorithm for a green supply chain network of mine industry: Case of Iranian mine industry," Operations Research Perspectives, Elsevier, vol. 5(C), pages 371-382.
    8. Dillon, Mary & Oliveira, Fabricio & Abbasi, Babak, 2017. "A two-stage stochastic programming model for inventory management in the blood supply chain," International Journal of Production Economics, Elsevier, vol. 187(C), pages 27-41.
    9. Keyvanshokooh, Esmaeil & Ryan, Sarah M. & Kabir, Elnaz, 2016. "Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition," European Journal of Operational Research, Elsevier, vol. 249(1), pages 76-92.
    10. Dai, Bo & Chen, Haoxun & Li, Yuan & Zhang, Yidong & Wang, Xiaoqing & Deng, Yuming, 2023. "An alternating direction method of multipliers for optimizing (s, S) policies in a distribution system with joint replenishment volume constraints," Omega, Elsevier, vol. 116(C).
    11. Mohammad Fattahi & Kannan Govindan, 2017. "Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products," Annals of Operations Research, Springer, vol. 253(1), pages 193-225, June.
    12. Hu, Zhengyang & Hu, Guiping, 2020. "Hybrid stochastic and robust optimization model for lot-sizing and scheduling problems under uncertainties," European Journal of Operational Research, Elsevier, vol. 284(2), pages 485-497.
    13. Rezapour, Shabnam & Allen, Janet K. & Mistree, Farrokh, 2016. "Reliable product-service supply chains for repairable products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 95(C), pages 299-321.
    14. Dai, B. & Chen, H.X. & Li, Y.A. & Zhang, Y.D. & Wang, X.Q. & Deng, Y.M., 2021. "Inventory replenishment planning of a distribution system with storage capacity constraints and multi-channel order fulfilment," Omega, Elsevier, vol. 102(C).

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