Case Study: Solving a Rental Fleet Sizing Model with a Large Time–Space Network

Abstract

This article addresses a rental fleet sizing problem (RFS) in the context of the truck rental industry, subject to uncertain customer travel time and nonstationary customer demand that is dependent on geographical location, time, and the economic cycle of the industry. We integrate tactical (asset purchases and sales) and operational (empty truck movement and vehicle assignment) decisions, with the explicit incorporation of an asset age factor, to achieve lower cost solutions. Typically, the length of time horizon and number of locations under consideration are quite large, which makes the RFS model computationally challenging to solve. Aggregation procedures are employed for location clustering and end-of-horizon effects are examined through demand scenario-based analyses. For the reduced time–space networks, decision analyses are conducted for the RFS model to provide insights into the truck rental business regarding asset movement decisions and asset procurement/disposal decisions over time and locations.