Migrating Towards Using Electric Vehicle on Campus-Proposed Method for Fleet Optimization

Managing a fleet efficiently to addresses demand within cost constraints is a challenge. Mismatched fleet size and demand can create suboptimal budget allocations and inconvenience users. To address this problem, many studies have been conducted around heterogeneous fleet optimization. That research has not included an examination of different vehicle types with travel distance constraints. This study focuses on optimizing the University of Tennessee (UT) motor pool which has a heterogeneous fleet that includes electric vehicles (EVs) with a travel distance and recharge time constraint. After assessing UT motor pool trip patterns, a Queuing model was used to estimate the maximum number of each vehicle type needed to minimize the expected customer wait time to near zero. The break-even point is used for optimization model to constrain the minimum number of years that electric vehicles should be operated under the no subsidy assumption. The models are very flexible and can be applied to a wide variety of fleet optimization problems. It can help fleet managers make decisions about fleet size and EV adoption. In the case of UT’s motor pool, the results show that the fleet has surplus vehicles. In addition to reducing the number of vehicles, total fleet costs could be minimized by using electric vehicles for all trips less than 100 miles.