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Migrating towards Using Electric Vehicles in Campus-Proposed Methods for Fleet Optimization

Authors: Taekwan Yoon; Christopher Cherry;

Migrating towards Using Electric Vehicles in Campus-Proposed Methods for Fleet Optimization

Abstract

Managing a fleet efficiently to address 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 as a case study, 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 the optimization model to constrain the minimum number of years that electric vehicles should be operated under the no-subsidy assumption. 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. The models are flexible and can be applied and help fleet managers make decisions about fleet size and EV adoption.

Keywords

Environmental effects of industries and plants, university motor pool, TJ807-830, TD194-195, Renewable energy sources, Environmental sciences, fleet optimization, fleet size and composition, electric vehicle adoption, queuing theory, GE1-350, fleet optimization; fleet size and composition; electric vehicle adoption; university motor pool; queuing theory

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    8
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
gold