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Evaluating the Effectiveness of a New Method for Computing Approximately Optimal ( s , S ) Inventory Policies

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  • James R. Freeland

    (Stanford University, Stanford, California)

  • Evan L. Porteus

    (Stanford University, Stanford, California)

Abstract

A new method for obtaining approximately optimal ( s , S ) inventory policies was presented recently as an illustration of a general approximation methodology proposed by Porteus. This paper seeks to evaluate that method numerically Our numerical work showed that when demand was normally distributed the method performed very well. The performance was good but diminished somewhat when demand followed a negative binomial distribution which was approximated by a normal distribution for purposes of the method. The empirical method of Wagner, O'Hagan and Lundh was evaluated concurrently. It also performed very well. Although its performance was not as good as that of the new method, it required less computational effort. The question of whether one of these approximate methods can provide improved performance in practical periodic review systems was raised. A partial answer was obtained by examining the performance of policies found by using a continuous review model. Both the new method and the empirical method outperformed these policies.

Suggested Citation

  • James R. Freeland & Evan L. Porteus, 1980. "Evaluating the Effectiveness of a New Method for Computing Approximately Optimal ( s , S ) Inventory Policies," Operations Research, INFORMS, vol. 28(2), pages 353-364, April.
    • Handle: RePEc:inm:oropre:v:28:y:1980:i:2:p:353-364
    DOI: 10.1287/opre.28.2.353
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    Cited by:

    1. Chan, Gin Hor & Song, Yuyue, 2003. "A dynamic analysis of the single-item periodic stochastic inventory system with order capacity," European Journal of Operational Research, Elsevier, vol. 146(3), pages 529-542, May.
    2. 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.
    3. Tarim, S. Armagan & Smith, Barbara M., 2008. "Constraint programming for computing non-stationary (R, S) inventory policies," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1004-1021, September.
    4. 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.

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