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.
Highlights
Managing a fleet efficiently to address demand within cost constraints is a challenge
This study examines fleet size and composition management, with a focus on the role of electric vehicles (EVs) in corporate passenger car fleets
Previous research [7] concludes that the EVs can reduce 38–41% of greenhouse gas (GHG) emissions compared to the conventional vehicles (CVs) and 7–12% of the emissions compared to traditional hybrids
Summary
A fleet management program balances many objectives, including driver management, speed management, fuel management, route management, fleet size, and composition management. Several earlier studies have examined fleet size and composition management [1,2,3,4,5], but none have addressed the unique operational characteristics of EVs in fleet optimization. With low emissions and lower operating costs (fuel and maintenance) than conventional vehicles (CVs), they are becoming more popular in commercial use [6]. This is despite the vehicles’ significantly different performance characteristics and fixed costs, such as purchase price, depreciation, refueling infrastructure, and registration fees. TThheerersetsot fotfhtishipsappaepr iesroirsgoarngizaendizaesdfoalslofwolslo: iwnsS:ecintioSnec2t,iwone 2w,iwll reewviielwl rpevreievwioupsrewvoiorkuss; winoSrekcst;ioinnsS3ecatniodn4s, w3 eanwdil4l ,dwesecrwibiellcduerrsecnritbfeleceut rarnedntitfls euestaganedanidtseusstiamgeataencdosetsftoirmeaatcehcvoeshtifcoler teyapceh; vinehSieccletitoynpe5;, iwn eSewctiilolnd5e,vweleopwitlhledemvoedloepl thhoewmtoodoelphtiomwizteo folpeetitmsiizzee flaenedt sciozme apnodsitcioomn;paonsidti,ofnin; aalnlyd,, firensaullltys, raensdulctosnacnlduscioonncoluf stihoins poaf ptheirswpialpl ebrewprilelsbeentperdesinenSteecdtiionnSse6ctaionnds76. and 7
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