Abstract
Reusable Articles (RAs) are vital for supply chain logistics as they provide for the safe, economical and environmentally friendly movement of products and materials. The existing literature related to RAs has focused on the design aspects, fleet sizing, allocation of RAs, repositioning of empty RAs, and their end-of-life stages. This paper focuses on the fleet-sizing problem of RAs under uncertain demand and turnaround times. Probability theory-based analytical models are developed to determine the optimal fleet size of RAs in both lost-sales and back-order cases. The proposed analytical procedure can be used to develop models to find the optimal fleet sizes for any demand scenario with a known distribution. In the lost-sales case, analytical models are developed for uniform and normally distributed demand scenarios. A simulation model is developed to test the effectiveness of the average on-hand inventory in service-level estimation. Sensitivity analysis of the average on-hand inventory for the changes in demand and turnaround times have also been conducted and the key managerial insights are summarized. Analytical models are also developed for the back-order case and some specific managerial insights are presented.
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