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A new method for joint replenishment problems

Author

Listed:
  • R Y K Fung

    (City University of Hong Kong)

  • X Ma

    (An'shan Institute of Iron and Steel Technology)

Abstract

This paper considers joint replenishment problems (JRP) of n items under deterministic and constant demand. Two new algorithms for JRP are proposed, based on a pair of tighter bounds for optimal cyclic time. The proposed algorithms can be used to determine the optimal cyclic policy and the optimal strict cyclic policy. Both algorithms are qualified for JRP with small major set-up costs, while only one of them can cope with JRP with any type of major set-up costs. Numerical experiments on randomly generated problems show that the new algorithms significantly outperform the existing exact algorithms for almost all of the test problems.

Suggested Citation

  • R Y K Fung & X Ma, 2001. "A new method for joint replenishment problems," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(3), pages 358-362, March.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:3:d:10.1057_palgrave.jors.2601091
    DOI: 10.1057/palgrave.jors.2601091
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    Citations

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    Cited by:

    1. Wang, Lin & He, Jing & Wu, Desheng & Zeng, Yu-Rong, 2012. "A novel differential evolution algorithm for joint replenishment problem under interdependence and its application," International Journal of Production Economics, Elsevier, vol. 135(1), pages 190-198.
    2. 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.
    3. 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.
    4. Bayindir, Z.P. & Birbil, S.I. & Frenk, J.B.G., 2004. "A Multi-Item Inventory Model With Joint Setup And Concave Production Costs," Econometric Institute Research Papers ERS-2004-044-LIS, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Yuen Ping Ho & Poh Kam Wong & Mun Heng Toh, 2009. "The Impact Of R&D On The Singapore Economy: An Empirical Evaluation," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 54(01), pages 1-20.
    6. 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.
    7. Porras, Eric & Dekker, Rommert, 2006. "An efficient optimal solution method for the joint replenishment problem with minimum order quantities," European Journal of Operational Research, Elsevier, vol. 174(3), pages 1595-1615, November.
    8. 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.
    9. Seyed Hamid Reza Pasandideh & Seyed Taghi Akhavan Niaki & Reza Abdollahi, 2020. "Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II," Operational Research, Springer, vol. 20(4), pages 2365-2396, December.
    10. Porras Musalem, E. & Dekker, R., 2005. "Generalized Solutions for the joint replenishment problem with correction factor," Econometric Institute Research Papers EI 2005-19, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. 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.
    12. 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.
    13. Bayindir, Z.P. & Birbil, S.I. & Frenk, J.B.G., 2004. "A Multi-Item Inventory Model With Joint Setup And Concave Production Costs," ERIM Report Series Research in Management ERS-2004-044-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    14. 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).
    15. 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.
    16. Tamar Cohen-Hillel & Liron Yedidsion, 2018. "The Periodic Joint Replenishment Problem Is Strongly 𝒩𝒫-Hard," Mathematics of Operations Research, INFORMS, vol. 43(4), pages 1269-1289, November.
    17. 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.
    18. 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.
    19. 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.

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