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Optimization in Fuzzy Inventory Model for Linearly Deteriorating Items, with Power Demand, Partial Backlogging and Linear Holding Cost

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
N. Rajeswari, T. Vanjikkodi, K. Sathyapriya
10.5120/ijca2017914259

N Rajeswari, T Vanjikkodi and K Sathyapriya. Optimization in Fuzzy Inventory Model for Linearly Deteriorating Items, with Power Demand, Partial Backlogging and Linear Holding Cost. International Journal of Computer Applications 169(1):6-12, July 2017. BibTeX

@article{10.5120/ijca2017914259,
	author = {N. Rajeswari and T. Vanjikkodi and K. Sathyapriya},
	title = {Optimization in Fuzzy Inventory Model for Linearly Deteriorating Items, with Power Demand, Partial Backlogging and Linear Holding Cost},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {1},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {6-12},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume169/number1/27947-2017914259},
	doi = {10.5120/ijca2017914259},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper a fuzzy inventory model is developed for deteriorating items with power demand rate. Shortages are allowed and partially backlogged. The holding cost is assumed to be time dependent. The cost components are considered as trapezoidal fuzzy numbers. The objective of this paper is to develop an inventory model in a fuzzy environment, minimize the total cost and thereby derive optimal policies. The total cost is defuzzified using Graded mean representation, and Signed distance methods. The values obtained by these methods are compared with the help of numerical examples. The convexity of the cost function is depicted graphically. The formulated model is tested for sensitivity by studying the effect of change in parameters.

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Keywords

Defuzzification, Deterioration, Graded mean representation method, Optimization, Partial backlogging, Power Demand, Shortages, Signed Distance Method, and Trapezoidal Fuzzy Number.