Next Article in Journal
Towards the Development of a Universal Expression for the Configurational Entropy of Mixing
Previous Article in Journal
Thermal Characteristics of a Primary Surface Heat Exchanger with Corrugated Channels
Previous Article in Special Issue
Choice Overload and Height Ranking of Menus in Partially-Ordered Sets
Article Menu

Export Article

Open AccessArticle
Entropy 2016, 18(1), 16; doi:10.3390/e18010016

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

Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Largo Lucio Lazzarino, Pisa 56122, Italy
Academic Editor: Kevin H. Knuth
Received: 17 October 2015 / Revised: 24 November 2015 / Accepted: 23 December 2015 / Published: 30 December 2015
(This article belongs to the Special Issue Entropy, Utility, and Logical Reasoning)
View Full-Text   |   Download PDF [272 KB, uploaded 30 December 2015]

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. View Full-Text
Keywords: maximum entropy principle; inventory; stochastic; optimization; heuristics; (r,Q) policy maximum entropy principle; inventory; stochastic; optimization; heuristics; (r,Q) policy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Castellano, D. Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle. Entropy 2016, 18, 16.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top