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On optimal algorithms for the joint replenishment problem

Author

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  • S Viswanathan

    (Nanyang Business School, Nanyang Technological University)

Abstract

In this paper, we demonstrate that the algorithm for determining the optimal strict cyclic policy for the Joint Replenishment Problem suggested by Fung and Ma1 does not guarantee an optimal solution. We propose a modification that will ensure that the algorithm obtains the optimal strict cyclic policy. We then perform a comprehensive computational study to compare the modified Fung and Ma algorithm with other optimal algorithms for the problem. The study reveals that the optimal algorithm of Viswanathan2 is computationally more efficient than other methods.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jorsoc:v:53:y:2002:i:11:d:10.1057_palgrave.jors.2601445
    DOI: 10.1057/palgrave.jors.2601445
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    Citations

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    Cited by:

    1. Porras Musalem, E. & Dekker, R., 2005. "New Bounds for the Joint Replenishment Problem: Tighter, but not always better," Econometric Institute Research Papers EI 2005-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    2. 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.
    3. A Nilsson & E A Silver, 2008. "A simple improvement on Silver's heuristic for the joint replenishment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(10), pages 1415-1421, October.
    4. Young Hyeon Yang & Jong Soo Kim, 2020. "An adaptive joint replenishment policy for items with non-stationary demands," Operational Research, Springer, vol. 20(3), pages 1665-1684, September.
    5. Nilsson, Andreas & Segerstedt, Anders & van der Sluis, Erik, 2007. "A new iterative heuristic to solve the joint replenishment problem using a spreadsheet technique," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 399-405, July.
    6. Monalisha Pattnaik & Padmabati Gahan, 2021. "Preservation effort effects on retailers and manufacturers in integrated multi-deteriorating item discrete supply chain model," OPSEARCH, Springer;Operational Research Society of India, vol. 58(2), pages 276-329, June.
    7. Porras Musalem, E. & Dekker, R., 2004. "On the efficiency of optimal algorithms for the joint replenishment problem: a comparative study," Econometric Institute Research Papers EI 2004-33, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    8. Tamar Cohen-Hillel & Liron Yedidsion, 2018. "The Periodic Joint Replenishment Problem Is Strongly š¯’©š¯’«-Hard," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1269-1289, November.
    9. Francisco Silva & Lucia Gao, 2013. "A Joint Replenishment Inventory-Location Model," Networks and Spatial Economics, Springer, vol. 13(1), pages 107-122, March.
    10. Porras, Eric & Dekker, Rommert, 2008. "A solution method for the joint replenishment problem with correction factor," International Journal of Production Economics, Elsevier, vol. 113(2), pages 834-851, June.
    11. E P Robinson & A Narayanan & L-L Gao, 2007. "Effective heuristics for the dynamic demand joint replenishment problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(6), pages 808-815, June.
    12. Khouja, Moutaz & Goyal, Suresh, 2008. "A review of the joint replenishment problem literature: 1989-2005," European Journal of Operational Research, Elsevier, vol. 186(1), pages 1-16, April.
    13. Bayindir, Z.P. & Birbil, S.I. & Frenk, J.B.G., 2006. "The joint replenishment problem with variable production costs," European Journal of Operational Research, Elsevier, vol. 175(1), pages 622-640, November.
    14. 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.
    15. Y Feng & S Viswanathan, 2007. "Impact of demand uncertainty on coordinating supply chain inventories through common replenishment epochs," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(7), pages 964-971, July.
    16. Amaya, Ciro Alberto & Carvajal, Jimmy & CastaƱo, 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.
    17. Yao, Ming-Jong & Lin, Jen-Yen & Lin, Yu-Liang & Fang, Shu-Cherng, 2020. "An integrated algorithm for solving multi-customer joint replenishment problem with districting consideration," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 138(C).
    18. Jen-Yen Lin & Ming-Jong Yao, 2020. "The joint replenishment problem with trade credits," Journal of Global Optimization, Springer, vol. 76(2), pages 347-382, February.
    19. 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.
    20. Olsen, Anne L., 2008. "Inventory replenishment with interdependent ordering costs: An evolutionary algorithm solution," International Journal of Production Economics, Elsevier, vol. 113(1), pages 359-369, May.
    21. Chiou, Chuang-Chun & Yao, Ming-Jong & Tsai, Jenteng, 2007. "A mutually beneficial coordination mechanism for a one-supplier multi-retailers supply chain," International Journal of Production Economics, Elsevier, vol. 108(1-2), pages 314-328, July.

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