Backorder fuzzy inventory model under function principle
Information Sciences: an International Journal
Genetic Algorithms and Manufacturing Systems Design
Genetic Algorithms and Manufacturing Systems Design
A periodic review inventory model involving fuzzy expected demand short and fuzzy backorder rate
Computers and Industrial Engineering
A Minimax Distribution Free Procedure for Fuzzy Mixed Periodic Review Inventory Models
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 02
A Fuzzy Bi-level Pricing Model and a PSO Based Algorithm in Supply Chains
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
International Journal of Systems Science - Computational Intelligence for Modelling and Control of Advanced Automotive Drivetrains
Multi-objective hybrid evolutionary algorithms for radial basis function neural network design
Knowledge-Based Systems
Mathematical and Computer Modelling: An International Journal
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
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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.