Title: An EOQ model with fuzzy demand and learning in fuzziness

Authors: Christoph H. Glock; Kurt Schwindl; Mohamad Y. Jaber

Addresses: Chair of Business Management and Industrial Management, Faculty of Economics, Julius-Maximilians-Universität Würzburg, Sanderring 2, 97070 Würzburg, Germany. ' Faculty of Business and Engineering, University of Applied Sciences Würzburg-Schweinfurt, Ignaz-Schoen-Str. 11, 97421 Schweinfurt, Germany. ' Department of Mechanical and Industrial Engineering, Ryerson University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada

Abstract: This paper develops an economic order quantity (EOQ) model with fuzzy demand that may vary between upper and lower limits. The imprecision in demand is assumed to reduce with time because of learning. The results from the developed model are compared to those of an EOQ model with fuzzy demand and no learning. It is shown that learning in fuzziness improves the information base for future orders by reducing uncertainty, which favours delivering demand in smaller lots which are delivered more frequently. As the learning rate increases and fuzziness in demand reduces, the results were shown to converge to those of the classical EOQ model.

Keywords: EOQ model; inventory control; fuzzy demand; learning; uncertainty reduction; economic order quantity.

DOI: 10.1504/IJSOM.2012.046675

International Journal of Services and Operations Management, 2012 Vol.12 No.1, pp.90 - 100

Published online: 23 Aug 2014 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article