Paper Info
Title
Stochastic Reorder Point-Lot Size (r, Q) Inventory Model under Maximum Entropy Principle.
Abstract
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.
Year
DOI
Venue
2016
10.3390/e18010016
ENTROPY
Keywords
Field
DocType
maximum entropy principle,inventory,stochastic,optimization,heuristics,(r,Q) policy
Minimax,Entropy rate,Mathematical optimization,Maximum entropy spectral estimation,Heuristic,Exact algorithm,Reorder point,Principle of maximum entropy,Statistics,Mathematics,Maximum entropy probability distribution
Journal
Volume
Issue
ISSN
18
1
1099-4300
Citations 
PageRank 
References 
0
0.34
16
Authors
1
Name
Order
Citations
PageRank
Davide Castellano1285.97