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The joint replenishment and freight consolidation of a warehouse in a supply chain

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  • Moon, I.K.
  • Cha, B.C.
  • Lee, C.U.

Abstract

We have developed joint replenishment and consolidated freight delivery policies for a third party warehouse that handles multiple items, which have deterministic demand rates in a supply chain. Two policies are proposed and mathematical models are developed to obtain the optimal parameters for the proposed policies. Four efficient algorithms are presented to solve the mathematical models for the two policies. The performances of the two policies with the parameters obtained from the proposed algorithms are then compared with the common cycle approach for 1600 randomly generated problems. The results show the robust performance of the proposed algorithm for both policies.

Suggested Citation

  • Moon, I.K. & Cha, B.C. & Lee, C.U., 2011. "The joint replenishment and freight consolidation of a warehouse in a supply chain," International Journal of Production Economics, Elsevier, vol. 133(1), pages 344-350, September.
  • Handle: RePEc:eee:proeco:v:133:y:2011:i:1:p:344-350
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    References listed on IDEAS

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    1. S Viswanathan, 2002. "On optimal algorithms for the joint replenishment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1286-1290, November.
    2. Wildeman, R.E. & Frenk, J.B.G. & Dekker, R., 1997. "An efficient optimal solution method for the joint replenishment problem," European Journal of Operational Research, Elsevier, vol. 99(2), pages 433-444, June.
    3. 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. Kyunghoon Kang & Ki-sung Hong & Ki Hong Kim & Chulung Lee, 2017. "Shipment Consolidation Policy under Uncertainty of Customer Order for Sustainable Supply Chain Management," Sustainability, MDPI, vol. 9(9), pages 1-17, September.
    2. Hanukov, Gabi, 2022. "A service system where junior servers approach a senior server on behalf of customers," International Journal of Production Economics, Elsevier, vol. 244(C).
    3. Hong, Ki-sung & Lee, Chulung, 2013. "Optimal time-based consolidation policy with price sensitive demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 275-284.
    4. 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.
    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. 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.

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