Research Article Open Access

PROBABILISTIC PERIODIC REVIEW M, N> INVENTORYMODELUSING LAGRANGE TECHNIQUE AND FUZZY ADAPTIVE PARTICLE SWARM OPTIMIZATION

H. A. Fergany1, N. A. El-Hefnawy2 and O. M. Hollah3
  • 1 Tanta University, Egypt
  • 2 Menoufia University, Egypt
  • 3 , Egypt

Abstract

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 <Qm, 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.

Journal of Mathematics and Statistics
Volume 10 No. 3, 2014, 368-383

DOI: https://doi.org/10.3844/jmssp.2014.368.383

Submitted On: 30 September 2013 Published On: 11 August 2014

How to Cite: Fergany, H. A., El-Hefnawy, N. A. & Hollah, O. M. (2014). PROBABILISTIC PERIODIC REVIEW M, N> INVENTORYMODELUSING LAGRANGE TECHNIQUE AND FUZZY ADAPTIVE PARTICLE SWARM OPTIMIZATION. Journal of Mathematics and Statistics, 10(3), 368-383. https://doi.org/10.3844/jmssp.2014.368.383

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Keywords

  • Inventory System
  • Periodic Review Model
  • Particle Swarm Optimization
  • Fuzzy Adaptive Particle Swarm Optimization