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A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains

A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains

Liya Wang, Vittal Prabhu
Copyright: © 2009 |Volume: 2 |Issue: 3 |Pages: 18
ISSN: 1935-5726|EISSN: 1935-5734|ISSN: 1935-5726|EISBN13: 9781616920692|EISSN: 1935-5734|DOI: 10.4018/jisscm.2009070101
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MLA

Wang, Liya, and Vittal Prabhu. "A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains." IJISSCM vol.2, no.3 2009: pp.1-18. http://doi.org/10.4018/jisscm.2009070101

APA

Wang, L. & Prabhu, V. (2009). A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains. International Journal of Information Systems and Supply Chain Management (IJISSCM), 2(3), 1-18. http://doi.org/10.4018/jisscm.2009070101

Chicago

Wang, Liya, and Vittal Prabhu. "A Stochastic Perturbation Algorithm for Inventory Optimization in Supply Chains," International Journal of Information Systems and Supply Chain Management (IJISSCM) 2, no.3: 1-18. http://doi.org/10.4018/jisscm.2009070101

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Abstract

In recent years, simulation optimization has attracted a great deal of attention because simulation can model the real systems in fidelity and capture complex dynamics. Among numerous simulation optimization algorithms, Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is an attractive approach because of its simplicity and efficiency. Although SPSA has been applied in several problems, it does not converge for some. This research proposes Augmented Simultaneous Perturbation Stochastic Approximation (ASPSA) algorithm in which SPSA is augmented to include research, ordinal optimization, non-uniform gain, and line search. Performances of ASPSA are tested on complex discrete supply chain inventory optimization problems. The tests results show that ASPSA not only achieves speed up, but also improves solution quality and converges faster than SPSA. Experiments also show that ASPSA is comparable to Genetic Algorithms in solution quality (6% to 15% worse) but is much more efficient computationally (over 12x faster).

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