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Replenish-up-to multi-chance-constraint inventory control system under fuzzy random lost-sale and backordered quantities

Taleizadeh, A. A ; Sharif University of Technology | 2013

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  1. Type of Document: Article
  2. DOI: 10.1016/j.knosys.2013.08.027
  3. Publisher: 2013
  4. Abstract:
  5. In this paper, a multiproduct multi-chance constraint stochastic inventory control problem is considered, in which the time-periods between two replenishments are assumed independent and identically distributed random variables. For the problem at hand, the decision variables are of integer-type, the service-level is a chance constraint for each product, and the space limitation is another constraint of the problem. Furthermore, shortages are allowed in the forms of fuzzy random quantities of lost sale that are backordered. The developed mathematical formulation of the problem is shown to be a fuzzy random integer-nonlinear programming model. The aim is to determine the maximum level of inventory for each product such that the total profit under budget and service level constraints is maximized. In order to solve the model, a hybrid method of fuzzy simulation, stochastic simulation, and particle swarm optimization approach (Hybrid FS-SS-PSO) is used. At the end, several numerical illustrations are given to demonstrate the applicability of the proposed methodology and to compare its performances with the ones of another hybrid algorithm as a combination of fuzzy simulation, stochastic simulation, and genetic algorithm (FS-SS-GA). The results of the numerical illustrations show that FS-SS-PSO performs better than FS-SS-GA in terms of both objective functions and CPU time
  6. Keywords:
  7. Genetic algorithm ; Inventory control ; Particle swarm optimization ; Stochastic replenishment intervals ; Fuzzy simulation ; Identically distributed random variables ; Integer-nonlinear programming ; Partial back-ordering ; Particle swarm optimization approaches ; Stochastic inventory controls ; Stochastic replenishments ; Stochastic simulations ; Computer simulation ; Fuzzy systems ; Particle swarm optimization (PSO) ; Stochastic models ; Stochastic systems ; Genetic algorithms
  8. Source: Knowledge-Based Systems ; Volume 53 , 2013 , Pages 147-156 ; 09507051 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0950705113002633