Copyright: Wyższa Szkola Logistyki, Poznań, Polska Citation: Indrajitsingha S.K., Routray S.S., Paikray S.K., Misra U., 2016, Fuzzy economic production quantity model with time dependent demand rate. LogForum 12 (3), 193-198, DOI: 10.17270/J.LOG.2016.3.1 URL: http://www.logforum.net/vol12/issue3/no1 Accepted: 07.5.2016, on-line: 7.07.2016. LogForum > Scientific Journal of Logistics < http://www.logforum.net p-ISSN 1895-2038 2016, 12 (3), 193-198 DOI: 10.17270/J.LOG.2016.3.1 e-ISSN 1734-459X FUZZY ECONOMIC PRODUCTION QUANTITY MODEL WITH TIME DEPENDENT DEMAND RATE Susanta Kumar Indrajitsingha 1 , Sudhansu Sekhar Routray 2 , Susanta Kumar Paikray 3 , Umakanta Misra 4 1) Berhampur University, Odisha, India, 2) Ravenshaw University, Cuttack, Odisha, India, 3) VSSUT, Burla, Odisha, India, 4) NIST, Golanthara, Berhampur, Odisha, India ABSTRACT. Background: In this paper, an economic production quantity model is considered under a fuzzy environment. Both the demand cost and holding cost are considered using fuzzy pentagonal numbers. The Signed Distance Method is used to defuzzify the total cost function. Methods: The results obtained by these methods are compared with the help of a numerical example. Sensitivity analysis is also carried out to explore the effect of changes in the values of some of the system parameters. Results and conclusions: The fuzzy EPQ model with time dependent demand rate was presented together with the possible implementation. The behavior of changes in parameters was analyzed. The possible extension of the implementation of this method was presented. Key words: Inventory, Pentagonal Fuzzy Number, Signed Distance Method. INTRODUCTION In real-life situations, exact data are often inadequate for a mathematical model. Inventory is a physical stock that a business keeps on hand in order to promote the smooth and efficient running of its affairs. But in practice, the effects of deterioration, shortages, holding cost, ordering cost etc. are important for inventory. Various types of uncertainties are involved in any inventory system. Historically, probability theory has been the primary test for representing uncertainty in mathematical models. Because of this, all the uncertainty was assumed to follow the characteristics of random uncertainty. A random process was one where the outcome of any particular realization of the process is strictly a matter of chance, and prediction of a sequence of events is not possible. Fuzzy set theory is an excellent tool for modeling the kind of uncertainty associated with vagueness, imprecision and the lack of information regarding a particular problem at hand. Initially, L.A. Zadeh [1963] introduced the concept of fuzzy sets. In this area a lot of research papers have been published by several researchers viz. S. K. Goyal [1985], Z.T. Balkhi [1998], K.J. Chung[2000], T. Chang [2003], Huang [2007], G.C. Mahata and A. Goswami [2010], S. K. Indrajitsingha et. al. [2015]. In this paper, we develop the EPQ model with a time-dependent demand rate using a pentagonal fuzzy number. The average total inventory costs in the fuzzy sense are derived. The parameters are fuzzified by pentagonal fuzzy number. The fuzzy model is defuzzified by using the Signed Distance Method.