Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

• Main Author: Davide Castellano
• Format: Article
• Language: English
• Published: MDPI AG 2015-12-01
• Series: Entropy
• Subjects: maximum entropy principle; inventory; stochastic; optimization; heuristics; (r,Q) policy
• Online Access: http://www.mdpi.com/1099-4300/18/1/16

This paper takes into account the continuous-review reorder point-lot size (r,Q) inventory model under stochastic demand, with the backorders-lost sales mixture. Moreover, to reflect the practical circumstance in which full information about the demand distribution lacks, we assume that only an estimate of the mean and of the variance is available. Contrarily to the typical approach in which the lead-time demand is supposed Gaussian or is obtained according to the so-called minimax procedure, we take a different perspective. That is, we adopt the maximum entropy principle to model the lead-time demand distribution. In particular, we consider the density that maximizes the entropy over all distributions with given mean and variance. With the aim of minimizing the expected total cost per time unit, we then propose an exact algorithm and a heuristic procedure. The heuristic method exploits an approximated expression of the total cost function achieved by means of an ad hoc first-order Taylor polynomial. We finally carry out numerical experiments with a twofold objective. On the one hand we examine the efficiency of the approximated solution procedure. On the other hand we investigate the performance of the maximum entropy principle in approximating the true lead-time demand distribution.