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Numerical Comparisons of Inventory Policies for Periodic Review Systems

Author

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  • Evan L. Porteus

    (Stanford University, Stanford, California)

Abstract

This paper studies the numerical computation of the two parameters (the reorder level s and the order up to level S ) of inventory policies for discrete time shortage cost systems. Our goal is to obtain approximately optimal policies with little computational effort. The paper introduces three new methods that are designed to achieve this goal. Two of the methods are shortcuts based on the method of Freeland and Porteus and one is a heuristic that makes several modifications to a standard continuous review approximation. The paper provides a fairly detailed survey of other methods for easily computing approximately optimal inventory policies. It then numerically compares all these methods on a reasonably broad range of problems. One of the shortcuts and the new heuristic method performed very well: the percentage error of their average costs was approximately 1%. Some commonly cited competing methods had percentage errors of over 10% and a commonly cited continuous review approximation had a percentage error of over 80%. To study the effect of extreme parameter choices in the test bed, the paper introduces a procedure to determine a subset of the parameter values, called the 1% contiguous test bed, for which each method performed well. The results show that, depending on the range of values that apply in a given practical situation, either (i) any of a large number of methods will yield good performance or (ii) a carefully selected method can achieve superior performance.

Suggested Citation

  • Evan L. Porteus, 1985. "Numerical Comparisons of Inventory Policies for Periodic Review Systems," Operations Research, INFORMS, vol. 33(1), pages 134-152, February.
  • Handle: RePEc:inm:oropre:v:33:y:1985:i:1:p:134-152
    DOI: 10.1287/opre.33.1.134
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    Cited by:

    1. Lyu, JrJung & Ding, Jyh-Hong & Chen, Ping-Shun, 2010. "Coordinating replenishment mechanisms in supply chain: From the collaborative supplier and store-level retailer perspective," International Journal of Production Economics, Elsevier, vol. 123(1), pages 221-234, January.
    2. G. P. Kiesmüller & K. Inderfurth, 2018. "Approaches for periodic inventory control under random production yield and fixed setup cost," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(2), pages 449-477, March.
    3. D. Beyer & S. P. Sethi, 1999. "The Classical Average-Cost Inventory Models of Iglehart and Veinott–Wagner Revisited," Journal of Optimization Theory and Applications, Springer, vol. 101(3), pages 523-555, June.
    4. V. Radhamani & B. Sivakumar & G. Arivarignan, 2022. "A Comparative Study on Replenishment Policies for Perishable Inventory System with Service Facility and Multiple Server Vacation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(1), pages 229-265, March.
    5. Babai, M.Z. & Jemai, Z. & Dallery, Y., 2011. "Analysis of order-up-to-level inventory systems with compound Poisson demand," European Journal of Operational Research, Elsevier, vol. 210(3), pages 552-558, May.
    6. Sandun C. Perera & Suresh P. Sethi, 2023. "A survey of stochastic inventory models with fixed costs: Optimality of (s, S) and (s, S)‐type policies—Discrete‐time case," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 131-153, January.
    7. Ben Jouida, Sihem & Guajardo, Mario & Klibi, Walid & Krichen, Saoussen, 2021. "Profit maximizing coalitions with shared capacities in distribution networks," European Journal of Operational Research, Elsevier, vol. 288(2), pages 480-495.
    8. Petrovic, Radivoj & Petrovic, Dobrila, 2001. "Multicriteria ranking of inventory replenishment policies in the presence of uncertainty in customer demand," International Journal of Production Economics, Elsevier, vol. 71(1-3), pages 439-446, May.
    9. 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.
    10. D. Beyer & S. P. Sethi, 1997. "Average Cost Optimality in Inventory Models with Markovian Demands," Journal of Optimization Theory and Applications, Springer, vol. 92(3), pages 497-526, March.
    11. Kleijnen, J.P.C. & Wan, J., 2007. "Optimization of simulated systems : OptQuest and alternatives [also see “Simulation for the optimization of (s, S) inventory system with random lead times and a service level constraint by using Arena," Other publications TiSEM ffaee312-9f6a-4452-9ccc-9, Tilburg University, School of Economics and Management.
    12. Chiang, Chi, 2008. "Periodic review inventory models with stochastic supplier's visit intervals," International Journal of Production Economics, Elsevier, vol. 115(2), pages 433-438, October.
    13. James Flynn, 2001. "Selecting review periods for a coordinated multi‐item inventory model with staggered deliveries," Naval Research Logistics (NRL), John Wiley & Sons, vol. 48(5), pages 430-449, August.
    14. Amiri-Aref, Mehdi & Klibi, Walid & Babai, M. Zied, 2018. "The multi-sourcing location inventory problem with stochastic demand," European Journal of Operational Research, Elsevier, vol. 266(1), pages 72-87.
    15. Xie, Xiaolan, 1998. "Stability analysis and optimization of an inventory system with bounded orders," European Journal of Operational Research, Elsevier, vol. 110(1), pages 126-149, October.
    16. 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.
    17. Bijvank, Marco & Vis, Iris F.A., 2011. "Lost-sales inventory theory: A review," European Journal of Operational Research, Elsevier, vol. 215(1), pages 1-13, November.
    18. James Flynn, 2000. "Selecting T for a periodic review inventory model with staggered deliveries," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 329-352, June.
    19. Wang, Minke & Wu, Jiang & Kafa, Nadine & Klibi, Walid, 2020. "Carbon emission-compliance green location-inventory problem with demand and carbon price uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    20. Baker, H. & Ehrhardt, R., 1995. "A dynamic inventory model with random replenishment quantities," Omega, Elsevier, vol. 23(1), pages 109-116, February.
    21. Dettenbach, Marcus & Thonemann, Ulrich W., 2015. "The value of real time yield information in multi-stage inventory systems – Exact and heuristic approaches," European Journal of Operational Research, Elsevier, vol. 240(1), pages 72-83.
    22. 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.

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