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The emerging prevalence of electric vehicles (EVs) in shared mobility services has led to a groundbreaking trend for decarbonizing the shared mobility sector. However, it is still unclear how to maximize the efficiency of EVs to reduce greenhouse gas (GHG) emissions while maintaining high service quality, particularly considering the ongoing transition towards a fully electrified service fleet. In this paper, focusing on meal delivery, we proposed an eco-friendly on-demand meal delivery (ODMD) system to maximize the utilities of EVs to mitigate GHG emissions and maintain low operational cost and delay cost. The main feature of our system is that its fleet consists of electric and gasoline vehicles mirroring the evolving electrification trend in the shared delivery sector. A rolling horizon framework integrated with the adaptive large neighborhood search (RHALNS) algorithm was proposed to efficiently solve the meal order dispatching and routing problem with the mixed fleet. Three delivery policies were explored in the numerical study. Experiment results demonstrated that it is necessary for online meal delivery platforms to actively collect information of electric vehicles and take initiative to employ an eco-friendly delivery policy.
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This content will become publicly available on December 1, 2026
Strategic bi-objective optimization for electric vehicle fleet replacement leveraging shared charging facilities
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 and private sectors. For example, the unoccupied time of a bus charging station can be leveraged to charge other electric vehicles (EVs). This shared usage model presents both opportunities and challenges for organizations considering transitions to electrified mobility. It is especially difficult when considering the variability in daily fleet operations and the availability of charging infrastructures. This paper presents a bi-objective optimization model 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 relevant for organizations managing vehicle fleets that are considering a strategic transition to EVs, with the goals of minimizing 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, utilizing shared charging facilities operated by the Utah Transit Authority. The results demonstrate effective strategies for replacing vehicles with varied operational characteristics, offering detailed plans and schedules that balance GHG emission reductions with operational efficiency. Additionally, we conducted a sensitivity analysis to assess the effects of different battery sizes, station disruptions, and traffic delays on the model’s outcomes and a feasibility analysis to prioritize the replacement of high-utility vehicles. Our research provides a foundation for fleet agencies to develop strategic EV replacement plans that consider multiple goals and leverage shared charging infrastructure, ultimately leading to optimized charging facility utilization and reduced maintenance costs. These strategies support more efficient, reliable, and sustainable operations in urban fleet systems.
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- Award ID(s):
- 2311954
- PAR ID:
- 10689635
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Computers, Environment and Urban Systems
- Volume:
- 122
- Issue:
- C
- ISSN:
- 0198-9715
- Page Range / eLocation ID:
- 102353
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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