An inventory model for a deteriorating item with displayed stock dependent demand under fuzzy inflation and time discounting over a random planning horizon

被引:35
作者
Roy, Arindam [1 ]
Maiti, Manas Kumar [2 ]
Kar, Samarjit [3 ]
Maiti, Manoranjan [4 ]
机构
[1] Haldia Inst Technol, Dept Engn Sci, Purba Medinipur 721657, WB, India
[2] Mahishadal Raj Coll, Dept Math, Purba Medinipur 721628, WB, India
[3] Natl Inst Technol, Dept Math, Durgapur 713209, WB, India
[4] Guru Nanak Inst Technol, Dept Math & Comp Applicat, Kolkata 700114, WB, India
关键词
Time value of moneys; Stochastic planning horizon; Possibility; Necessity; REPLENISHMENT; CONSTRAINTS; SHORTAGES; SYSTEMS; NUMBERS;
D O I
10.1016/j.apm.2007.12.015
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
An inventory model for a deteriorating item (seasonal product) with linearly displayed stock dependent demand is developed in imprecise environment (involving both fuzzy and random parameters) under inflation and time value of money. It is assumed that time horizon, i.e., period of business is random and follows exponential distribution with a known mean. The resultant effect of inflation and time value of money is assumed as fuzzy in nature. The particular case, when resultant effect of inflation and time value is crisp in nature, is also analyzed. A genetic algorithm (GA) is developed with roulette wheel selection, arithmetic crossover, random mutation. For crisp inflation effect, the total expected profit for the planning horizon is maximized using the above GA to derive optimal inventory decision. On the other hand when inflationary effect is fuzzy then the above expected profit is fuzzy in nature too. Since optimization of fuzzy objective is not well defined, the optimistic/pessimistic: return of the expected profit is obtained using possibility/necessity measure of fuzzy event. Fuzzy simulation process is proposed to deter-mine this optimistic/pessimistic return. Finally a fuzzy simulation based GA is developed and is used to maximize the above optimistic/pessimistic return to get optimal decision. The models are illustrated with some numerical examples and some sensitivity analyses have been presented. (C) 2008 Elsevier Inc. All rights reserved.
引用
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页码:744 / 759
页数:16
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