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			<titleStmt><title level='a'>Strategic bi-objective optimization for electric vehicle fleet replacement leveraging shared charging facilities</title></titleStmt>
			<publicationStmt>
				<publisher>Elsevier</publisher>
				<date>12/01/2025</date>
			</publicationStmt>
			<sourceDesc>
				<bibl> 
					<idno type="par_id">10689635</idno>
					<idno type="doi">10.1016/j.compenvurbsys.2025.102353</idno>
					<title level='j'>Computers, Environment and Urban Systems</title>
<idno>0198-9715</idno>
<biblScope unit="volume">122</biblScope>
<biblScope unit="issue">C</biblScope>					

					<author>Shouzheng Pan</author><author>Ran Wei</author><author>Xiaoyue Cathy Liu</author><author>Jeff Phillips</author><author>Bei Wang</author>
				</bibl>
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		<profileDesc>
			<abstract><ab><![CDATA[Electrification of vehicle fleets has advanced significantly in recent years to achieve net-zero greenhouse gas(GHG) emissions. As a cost-effective strategy, shared charging facilities are increasingly used by public andprivate sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge otherelectric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizationsconsidering transitions to electrified mobility. It is especially difficult when considering the variability in dailyfleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimizationmodel designed to strategically guide the replacement of vehicle fleets with EV. The model aligns the spatial-temporal dynamics of vehicle routes with the availability of shared charging facilities. It is particularly relevantfor organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals ofminimizing GHG emissions from fuel consumption and vehicle idling, and reducing operational delays (e.g.detour and charging time for the EV fleet). We applied this model to the University of Utah campus fleet, utilizingshared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies forreplacing vehicles with varied operational characteristics, offering detailed plans and schedules that balanceGHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assessthe effects of different battery sizes, station disruptions, and traffic delays on the model’s outcomes and afeasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation forfleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage sharedcharging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenancecosts. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems.]]></ab></abstract>
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