Paper Info
Title
Two-storage inventory model with lot-size dependent fuzzy lead-time under possibility constraints via genetic algorithm
Abstract
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.
Year
DOI
Venue
2007
10.1016/j.ejor.2006.03.029
European Journal of Operational Research
Keywords
Field
DocType
Region reducing genetic algorithm,Chance constrained programming,Fuzzy lead time,Two-storage inventory
Mathematical optimization,Economic order quantity,Inversion (meteorology),Fuzzy set operations,Fuzzy logic,Algorithm,Inventory control,Lead time,Numerical analysis,Operations management,Mathematics,Genetic algorithm
Journal
Volume
Issue
ISSN
179
2
0377-2217
Citations 
PageRank 
References 
15
0.85
2
Authors
2
Name
Order
Citations
PageRank
Manas Kumar Maiti123620.68
Manoranjan Maiti251447.77