Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/427498
Title: Optimization of Fuzzy Economic Order Quantity Models for Deteriorating Items
Researcher: Poswal, Preety
Guide(s): Rajoria, Yogendra Kumar and Chauhan, Anand
Keywords: Mathematics
Physical Sciences
University: K.R. Mangalam Univeristy, Gurgaon
Completed Date: 2020
Abstract: One of the key revolutions in mathematics and science in this century is the theory of uncertainty, newlinewhich changed from a traditional perspective to a modern perspective in the 19th century. Fuzzy newlineset theory gives us the chance to meaningfully explain vague concepts in everyday language. The newlinepresent study was carried out to investigate inventory system concepts and apply them to the newlinedevelopment of inventory models in a fuzzy environment. A fuzzy set theory has been found to newlinehave a significant role in covering a broad spectrum of theoretical and practical study areas in newlinemedicine, mathematics, business, social science, and the natural sciences. In the present study, we newlinehave attempted to incorporate some fuzzy inventory models with different business environments newlinewith different types of deterioration rates, effect of inflation, and permissible delay, with various newlinedemand parameters. Demand uncertainty occurs when a business or trade seems unable to newlineeffectively estimate customer demand for its products. Therefore, in this study, the uncertain newlinedemand factor is dependent on stock and price dependent, and also the effect of inflation with newlinepreservation technology is considered. A fuzzy inventory model with a Weibull deterioration rate, newlinea quadratic demand rate, and a variable holding cost under permissible shortages has been newlinedeveloped. Costs such as ordering, shortage, and deterioration are addressed as pentagonal fuzzy newlinenumbers that are defuzzified using a graded mean representation approach. An optimal policy is newlineto develop an optimal inventory policy for decision makers to estimate medicinal deterioration newlineproducts for the healthcare industry in a fuzzy environment. newline
Pagination: XIII, 202
URI: http://hdl.handle.net/10603/427498
Appears in Departments:Department of Mathematics

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