PROBABILISTIC PERIODIC REVIEWINVENTORYMODELUSING LAGRANGE TECHNIQUE AND FUZZY ADAPTIVE PARTICLE SWARM OPTIMIZATION | Science Publications

Journal of Mathematics and Statistics

N. A. El-Hefnawy¹  H. A. Fergany¹  O. M. Hollah¹ 

关键词: Inventory System;    Periodic Review Model;    Particle Swarm Optimization;    Fuzzy Adaptive Particle Swarm Optimization;   

DOI  :  10.3844/jmssp.2014.368.383

学科分类：社会科学、人文和艺术（综合）

来源: Science Publications

【 摘 要 】

The integration between inventory model and Artificial Intelligent (AI) represents the rich area of research since last decade. In this study we investigate probabilistic periodic review m, N> inventory model with mixture shortage (backorder and lost sales) using Lagrange multiplier technique and Fuzzy Adaptive Particle Swarm Optimization (FAPSO) under restrictions. The objective of these algorithms is to find the optimal review period and optimal maximum inventory level which will minimize the expected annual total cost under constraints. Furthermore, a numerical example is applied and the experimental results for both approaches are reported to illustrate the effectiveness of overcoming the premature convergence and of improving the capabilities of searching to find the optimal results in almost all distributions.

【 授权许可】

Unknown