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A genetic algorithm to optimize multiproduct multiconstraint inventory control systems with stochastic replenishment intervals and discount

Taleizadeh, A. A ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1007/s00170-010-2604-8
  3. Publisher: 2010
  4. Abstract:
  5. There are two main assumptions in multiperiodic inventory control problems. The first is the continuous review, where, depending on the inventory level, orders can happen at any time, and the other is the periodic review, where orders can only happen at the beginning of each period. In this paper, these assumptions are relaxed, and the periods between two replenishments are assumed independent and identically distributed random variables. Furthermore, the decision variables are assumed integer-type and that there are two kinds of space and budget constraints. The incremental discounts to purchase products are considered, and a combination of backorder and lost sales are taken into account for the shortages. The model of this problem is shown to be a mixed integer-nonlinear programming type, and in order to solve it, both genetic algorithm and simulated annealing approaches are employed. At the end, two numerical examples are given to demonstrate the applicability of the proposed methodologies in which genetic algorithm method performs better than simulated annealing in terms of objective function values
  6. Keywords:
  7. Mixed integer-nonlinear programming ; Backorders ; Budget constraint ; Continuous review ; Decision variables ; Discount ; Identically distributed random variables ; Inventory control problems ; Inventory levels ; Lost sale ; Mixed integer ; Multi-constraints ; Multi-products ; Numerical example ; Objective function values ; Periodic review ; Stochastic replenishment ; Annealing ; Computer simulation ; Genetic algorithms ; Integer programming ; Inventory control ; Nonlinear programming ; Random variables ; Stochastic systems ; Simulated annealing
  8. Source: International Journal of Advanced Manufacturing Technology ; Volume 51, Issue 1-4 , November , 2010 , Pages 311-323 ; 02683768 (ISSN)
  9. URL: http://link.springer.com/article/10.1007%2Fs00170-010-2604-8