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Optimal and Heuristic Decisions in Single-and Multi-Item Inventory Systems

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  • Eliezer Naddor

    (The Johns Hopkins University)

Abstract

A comparison of optimal solutions of single-item inventory systems for the three classical policies tZ, sq, and sZ shows that the optimal order levels in the tZ and sZ policies are usually the same, and so are the optimal reorder points in the sq and sZ policies. The total cost for each of the three policies can be expressed as that of a fundamental model using an appropriate equivalent distribution. The fundamental model can be analyzed with ease. It also provides closed-form results for the normal distribution. Heuristic rules are presented allowing the analysis of inventory systems on the basis of their average demand, standard deviation of demand, probability of no demand, lead-time, carrying cost, replenishing cost, and an availability index. Compared with the optimal decisions and costs for identical inventory systems, in which the probability distribution of demand is known, the heuristic rules give excellent results. The optimal and heuristic decisions are extended to multi-item systems in which there is a cost for placing and receiving an order for a family of items every reviewing period. An algorithm is presented for finding the optimal reviewing period and the corresponding decisions and costs. The heuristic decisions compare very favorably with the optimal ones.

Suggested Citation

  • Eliezer Naddor, 1975. "Optimal and Heuristic Decisions in Single-and Multi-Item Inventory Systems," Management Science, INFORMS, vol. 21(11), pages 1234-1249, July.
    • Handle: RePEc:inm:ormnsc:v:21:y:1975:i:11:p:1234-1249
    DOI: 10.1287/mnsc.21.11.1234
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    Cited by:

    1. Bichescu, Bogdan C. & Fry, Michael J., 2009. "A numerical analysis of supply chain performance under split decision rights," Omega, Elsevier, vol. 37(2), pages 358-379, April.
    2. Kevin H. Shang & Sean X. Zhou & Geert-Jan van Houtum, 2010. "Improving Supply Chain Performance: Real-Time Demand Information and Flexible Deliveries," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 430-448, May.
    3. Lagodimos, A.G. & Christou, I.T. & Skouri, K., 2012. "Computing globally optimal (s,S,T) inventory policies," Omega, Elsevier, vol. 40(5), pages 660-671.
    4. Sean X. Zhou & Chaolin Yang, 2016. "Continuous-Review ( R, nQ ) Policies for Inventory Systems with Dual Delivery Modes," Operations Research, INFORMS, vol. 64(6), pages 1302-1319, December.
    5. Hon‐Shiang Lau & Amy Hing‐Ling Lau, 2003. "Nonrobustness of the normal approximation of lead‐time demand in a (Q, R) system," Naval Research Logistics (NRL), John Wiley & Sons, vol. 50(2), pages 149-166, March.
    6. Altay, Nezih & Litteral, Lewis A. & Rudisill, Frank, 2012. "Effects of correlation on intermittent demand forecasting and stock control," International Journal of Production Economics, Elsevier, vol. 135(1), pages 275-283.
    7. Zied Babai, M. & Syntetos, Aris A. & Teunter, Ruud, 2010. "On the empirical performance of (T, s, S) heuristics," European Journal of Operational Research, Elsevier, vol. 202(2), pages 466-472, April.
    8. Altay, Nezih & Rudisill, Frank & Litteral, Lewis A., 2008. "Adapting Wright's modification of Holt's method to forecasting intermittent demand," International Journal of Production Economics, Elsevier, vol. 111(2), pages 389-408, February.
    9. Baker, H. & Ehrhardt, R., 1995. "A dynamic inventory model with random replenishment quantities," Omega, Elsevier, vol. 23(1), pages 109-116, February.

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