Comparing traditional and fuzzy-set solutions to Q, r inventory systems with discrete lead-time distributions

Authors:
Xinmin Wu;Donald P. Warsing, Jr.
Affiliations:
SAS Institute Inc., Cary, NC, USA;Department of Business Management, North Carolina State University, Raleigh, NC, USA
Venue:
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Year:
2013

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Abstract

Using a previously published approach to computing Q, r policies for an inventory system with uncertain parameters described by fuzzy sets, we compare thee methods for specifying lead-time demand for four different empirically-specified, non-normal distributions of replenishment lead time. This general distribution of lead time results in a situation in which the distribution of demand over the lead time, or lead-time demand LTD, is not easily specified. We compare Q, r policies generated by using a traditional normal approximation to LTD, a fuzzy-set approximation, and the optimal policy computed via a simulation-optimization approach that utilizes the explicit LTD distribution. We show that, on average, the results from the fuzzy-set model are significantly more accurate than the traditional normal approximation, especially when the LTD distribution is highly skewed.