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A heuristic framework based on linear programming to solve the constrained joint replenishment problem (C-JRP)

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  • Amaya, Ciro Alberto
  • Carvajal, Jimmy
  • Castaño, Fabian

Abstract

This paper presents a new approach to solve the joint replenishment problem under deterministic demand and resource constraints (C-JRP). To solve the problem, a heuristic framework based on linear programming is presented. The proposed method can be extended to solve different versions of the problem and can be extended with linear programming modeling. This method is analyzed and some tests are performed, the results of which show that the proposed algorithm outperforms other well-known algorithms in total cost.

Suggested Citation

  • Amaya, Ciro Alberto & Carvajal, Jimmy & Castaño, Fabian, 2013. "A heuristic framework based on linear programming to solve the constrained joint replenishment problem (C-JRP)," International Journal of Production Economics, Elsevier, vol. 144(1), pages 243-247.
    • Handle: RePEc:eee:proeco:v:144:y:2013:i:1:p:243-247
    DOI: 10.1016/j.ijpe.2013.02.008
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    References listed on IDEAS

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    8. Hoque, M.A., 2006. "An optimal solution technique for the joint replenishment problem with storage and transport capacities and budget constraints," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1033-1042, December.
    9. Cha, B.C. & Moon, I.K. & Park, J.H., 2008. "The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(5), pages 720-730, September.
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    Cited by:

    1. He-Yau Kang & Amy H.I. Lee & Chien-Wei Wu & Cheng-Han Lee, 2017. "An efficient method for dynamic-demand joint replenishment problem with multiple suppliers and multiple vehicles," International Journal of Production Research, Taylor & Francis Journals, vol. 55(4), pages 1065-1084, February.
    2. Ji Seong Noh & Jong Soo Kim & Biswajit Sarkar, 2019. "Stochastic joint replenishment problem with quantity discounts and minimum order constraints," Operational Research, Springer, vol. 19(1), pages 151-178, March.
    3. Nguyen, Christine & Dessouky, Maged & Toriello, Alejandro, 2014. "Consolidation strategies for the delivery of perishable products," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 108-121.
    4. Marcello Braglia & Davide Castellano & Marco Frosolini, 2016. "Joint-replenishment problem under stochastic demands with backorders-lost sales mixtures, controllable lead times, and investment to reduce the major ordering cost," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(8), pages 1108-1120, August.
    5. Cui, Ligang & Deng, Jie & Liu, Rui & Xu, Dongyang & Zhang, Yajun & Xu, Maozeng, 2020. "A stochastic multi-item replenishment and delivery problem with lead-time reduction initiatives and the solving methodologies," Applied Mathematics and Computation, Elsevier, vol. 374(C).
    6. Carvajal, Jimmy & Castaño, Fabian & Sarache, William & Costa, Yasel, 2020. "Heuristic approaches for a two-echelon constrained joint replenishment and delivery problem," International Journal of Production Economics, Elsevier, vol. 220(C).
    7. Subrata Mitra & Balram Avittathur, 2018. "Application of linear programming in optimizing the procurement and movement of coal for an Indian coal-fired power-generating company," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(3), pages 207-224, September.

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