Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

Martins, Leandro do C.; Tordecilla, Rafael D.; Castaneda, Juliana; Juan, Ángel A.; Faulín, Javier

doi:10.3390/en14165131

Cited by 23 publications

(12 citation statements)

References 176 publications

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“…In this problem, the maximization of the tourist satisfaction is considered while meeting path constraints such as total length, visiting times at each POI, or budget constraints. Extensions of the TTDP include the use of uncertain scenarios [32], hybrid fleets [2], and multi-period fleets [33]. To the best of our knowledge, Cassia et al [34] is the first study to consider the EVSPP with both user preferences as POIs and time windows.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Path Planner for Electric Vehicles considering User Defined Time Windows and Preferences

Cubillos¹,

Dell’Amico²,

Jabali³

et al. 2023

Preprint

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A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient solution algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We illustrate the use of the web interface, giving usage examples and comparing different settings.

show abstract

Section: Related Workmentioning

confidence: 99%

An Enhanced Path Planner for Electric Vehicles considering User Defined Time Windows and Preferences

Cubillos¹,

Dell’Amico²,

Jabali³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, the use of electric batteries typically present driving-range limitations and long re-charging times. Martins et al [82] present a review on EV routing. The paper describes a current trend in the literature of integrating real-life characteristics such as partial recharging, battery swapping, allocation of charging stations, horizontal cooperation strategies, backhauling practices, hybrid gas and electric fleets, and battery durability.…”

Section: Energy Optimization Using Electric Vehiclesmentioning

confidence: 99%

Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy

et al. 2023

Self Cite

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Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.

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“…This includes a 6.2% increase in fully electric vehicles [1]. Indeed, EVs are perceived to be a sustainable alternative to traditional combustion engine vehicles, offering advantages in terms of performance and reduced emissions [2]. However, a number of barriers impede a wider EV uptake.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences

et al. 2023

View full text Add to dashboard Cite

A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path-planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient heuristic algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are the following: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We demonstrate the use of the web interface, giving usage examples and comparing different settings.

show abstract

Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation

Cited by 23 publications

References 176 publications

An Enhanced Path Planner for Electric Vehicles considering User Defined Time Windows and Preferences

An Enhanced Path Planner for Electric Vehicles considering User Defined Time Windows and Preferences

Optimizing Energy Consumption in Smart Cities’ Mobility: Electric Vehicles, Algorithms, and Collaborative Economy

An Enhanced Path Planner for Electric Vehicles Considering User-Defined Time Windows and Preferences

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