Affiliations: SAS Institute Inc., Cary, NC, USA | Department of Business Management, North Carolina State University, Raleigh, NC, USA
Note: [] Corresponding author. Xinmin Wu, SAS Institute Inc., 100 SAS Campus Drive, Cary, NC 27513, USA. Tel.: +1 919 607 6648; E-mail: [email protected]
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.
