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FUZZY ECONOMIC PRODUCTION QUANTITY MODEL FOR ITEMS WITH IMPERFECT QUALITY.pdf

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FUZZY ECONOMIC PRODUCTION QUANTITY MODEL FOR ITEMS WITH IMPERFECT QUALITY

International Journal of Innovative Computing, Information and Control ICIC International c°2007 ISSN 1349-4198 Volume 3, Number 1, February 2007 pp. 85—95 FUZZY ECONOMIC PRODUCTION QUANTITY MODEL FOR ITEMS WITH IMPERFECT QUALITY Shan Huo Chen Department of Information Management Ching Yun University 229, Chien-Hsin Rd., Jung-Li 320, Taiwan shchen im@.tw Chien-Chung Wang Department of Finance, Management College National Defense University 70, Sec.2, Ehongyang North Rd., Peitou District, Taipei 11258, Taiwan jameswangcc@ Shu Man Chang Department of Shipping Business Management China College of Marine Technology and Commerce 212, Sec.9, Yan-Pin N. Rd., Taipei 111, Taiwan smchang@.tw Received July 2006; revised October 2006 Abstract. In the real world, vague phenomenon is quite common in the production/inv- entory models. In order to process the vagueness, a production/inventory model that can be more closely related to the real vagueness and can take account of the vague factors that contribute to production costs, is required. The model must be extended or altered to fit in with the fuzzy situation. Since items with imperfect quality, during produc- tion or inventory procedure, are unavoidable, we also consider this situation. In order to treat the case in the vague environment, we propose a Fuzzy Economic Production Quantity (FEPQ) model with imperfect products that can be sold at a discount price. In this model, costs and quantities are expressed as trapezoidal fuzzy numbers. Moreover, we use Function Principle to manipulate arithmetical operations, Graded Mean Integra- tion Representation method to defuzzify, and Kuhn-Tucker conditions to find the optimal economic production quantity of the fuzzy production inventory model. Finally, an ap- plication of an electronics industry example gives a satisfactory result. Keywords: Fuzzy inventory model, Economic production quantity, Function principle, Graded mean integration representation method, Optimization, Imperfect