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A hybrid method of pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint inventory control systems with random fuzzy replenishments

Taleizadeh, A. A ; Sharif University of Technology | 2009

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  1. Type of Document: Article
  2. DOI: 10.1016/j.mcm.2008.10.013
  3. Publisher: 2009
  4. Abstract:
  5. Multi-periodic inventory control problems are mainly studied employing one of two assumptions. The first is the continuous review, where depending on the inventory level, orders can be placed at any time, and the other is the periodic review, where orders can be placed only at the beginning of each period. In this paper, we relax these assumptions and assume that the time-periods between two replenishments are random fuzzy variables. While in the model of the problem at hand the decision variables are of integer type and there are space and service level constraints, for the shortages we consider a combination of back-order and lost-sales. We show the model of this problem to be an integer-nonlinear-programming type and in order to solve it, a hybrid method of Pareto, TOPSIS and Genetic Algorithm approach is used. At the end, a numerical example is given to demonstrate the applicability of the proposed methodology. © 2008 Elsevier Ltd. All rights reserved
  6. Keywords:
  7. Algorithms ; Constrained optimization ; Control theory ; Dynamic programming ; Genetic algorithms ; Genetic engineering ; Hybrid sensors ; Hybrid systems ; Integer programming ; Inventory control ; Nonlinear programming ; Pareto principle ; Random variables ; Fuzzy random replenishment ; Genetic algorithm ; Integer-nonlinear programming ; Pareto ; Replenishment ; TOPSIS ; Control systems
  8. Source: Mathematical and Computer Modelling ; Volume 49, Issue 5-6 , 2009 , Pages 1044-1057 ; 08957177 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S0895717708003750