Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 179, Issue 2, Pages 352-371Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.ejor.2006.03.029
Keywords
region reducing genetic algorithm; chance constrained programming; fuzzy lead time; two-storage inventory
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Multi-item inventory models with stock dependent demand and two storage facilities are developed in a fuzzy environment where processing time of each unit is fuzzy and the processing time of a lot is correlated with its size. These are order-quantity reorder-point models with back-ordering if required. Here possibility and crisp constraints on investment and capacity of the small storehouse respectively are considered. The models are formulated as fuzzy chance constrained programming problem and is solved via generalized reduced gradient (GRG) technique when crisp equivalent of the constraints are available. A genetic algorithm (GA) is developed based on fuzzy simulation and entropy where region of search space gradually decreases to a small neighborhood of the optima and it is used to solve the models whenever the equivalent crisp form of the constraint is not available. The models are illustrated with some numerical examples and some sensitivity analyses have been done. For some particular cases results observed via GRG and GA are compared. (c) 2006 Elsevier B.V. All rights reserved.
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