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Inventory model of deteriorating items with two-warehouse and stock dependent demand using genetic algorithm in fuzzy environment. (English) Zbl 1289.90004

The authors present a multi-item inventory model for deteriorating items with stock dependent demand under two-warehouse system in fuzzy environment. Joint replenishment and simultaneous transfer of items from one warehouse to another is proposed using basic period (BP) policy. In previous two-storage multi-item inventory models it was observed that items are ordered and transferred from one storage to another individually, which incurred a large amount of ordering and transportation cost. In this model items are ordered and transferred from one warehouse to another simultaneously using BP policy. Due to the preserving condition of warehouses, items gradually lose their utility (deterioration takes place) and this realistic phenomenon is incorporated in this model. As some parameters are fuzzy in nature, objective (average profit) functions, as well as some constraints, are imprecise in nature. The goal of the proposed model is to optimize the possibility/necessity measure of the fuzzy goal of the objective functions under the constraints that reflect some pre-defined necessity.
A genetic algorithm (GA) is used to solve the proposed model. The authors follow the idea of existing genetic algorithm approach named Contractive Mapping Genetic Algorithm (CMGA). In the CMGA, a movement from the old population to a new one takes place only if an average fitness of the new population is better than the fitness of the old one. Adequate solution encoding, fitness evaluation and genetic operators are implemented. Due to the complexity of the considered model, the performance of the proposed GA is illustrated on a simple numerical example with three items.

MSC:

90-08 Computational methods for problems pertaining to operations research and mathematical programming
90B05 Inventory, storage, reservoirs
68T20 Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.)
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