Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption

Xiao, Yiyong; Zhang, Yue; Kaku, Ikou; Kang, Rui; Pan, Xing

doi:10.1016/j.rser.2021.111567

Cited by 167 publications

(67 citation statements)

References 55 publications

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“…Therefore, Froger et al [46] considered a realistic nonlinear relationship between the time spent on refueling and the amount of fuel consumed by the vehicle. Moreover, Xiao et al [47] proposed a new model of the electric vehicle routing problem with respect to a general energy/electricity consumption function for EVs that factor in energy losses, nonlinear charging function with the piecewise linearization technique, efficient visits to charge stations, and continuous decision variables for speed, payload, travel time, recharging, etc. Karakatic [48] developed a Two-Layer Genetic Algorithm (TLGA) to overcome the capacitated Multi-Depot Vehicle Routing Problem by considering Time Windows (MD-VRPTW) and Electric Vehicles (EV) with partial nonlinear recharging times.…”

Section: ) Charging Typementioning

confidence: 99%

Green Vehicle Routing Problem: State of the Art and Future Directions

Sabet

Farooq

2022

IEEE Access

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Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions reduction, while routing the vehicles. It can be either through adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil fuel vehicles in fleets. GVRP also takes into account environmental sustainability in transportation and logistics. We critically review several variations and specializations of GVRP to address issues related to charging, pick-up, delivery, and energy consumption. Starting with the concepts and definitions of GVRP, we summarize the key elements and contributors to GVRP publications. Afterward, the issues regarding each category of green vehicle routing are reviewed, based on which key future research directions and challenges are suggested. It was observed that the main focus of previous publications is on the operational level routing decision and not the supply chain issues. The majority of publications used metaheuristic methods, while overlooking the emerging machine learning methods. We envision that in addition to machine learning, reinforcement learning, distributed systems, internet of vehicles (IoV), and new fuel technologies have a strong role in developing the GVRP research further.

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Section: ) Charging Typementioning

confidence: 99%

Green Vehicle Routing Problem: State of the Art and Future Directions

Sabet

Farooq

2022

IEEE Access

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“…While deterministic EV-VRP/DRT problems have been widely studied in the past, stochastic and dynamic EV-DRT problems are more challenging and have recently received increasing attention [18,40]. For deterministic problems, the reader is referred to the recent reviews by Asghari et al [38], Kucukoglu et al [14], and Xiao et al [44].…”

Section: Charging Schedulingmentioning

confidence: 99%

Survey of charging management and infrastructure planning for electrified demand-responsive transport systems: Methodologies and recent developments

Fang

2022

Eur. Transp. Res. Rev.

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The accelerated electrification of transport systems with EVs has brought new challenges for charging scheduling, fleet management, and charging infrastructure location and configuration planning. In this review, we have provided a systematic review of the recent development in strategic, tactical, and operational decisions for demand responsive transport system planning using electric vehicles (EV-DRT). We have summarized recent developments in mathematical modeling approaches by focusing on the problems of dynamic EV-DRT optimization, fleet sizing, and charging infrastructure planning. A list of existing open-access datasets, numerical test instances, and software are provided for future research in EV-DRT and related problems. Current research gaps are identified and future research directions are discussed.

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“…Different cultures, conflicting interests of stakeholders [25], the rapid growth of transportation dynamic elements, and policies are just some of the challenges to promote sustainable urban development [26][27][28][29][30]. Extensive studies have been conducted so far about the abovementioned aspects [31]. However, identifying which factors are the most important ones for urban transportation sustainability is still a matter of debate among researchers [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Mixed Transport Network Prioritization Based on Environmental Impact and Population Density

Mousavimasouleh

Salehi

Sadeghi

et al. 2022

Journal of Advanced Transportation

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Today, due to the growing importance of sustainable development in urban areas, the decision to prioritize different transportation options in an area where users have to combine different transportation modes has received much attention in the scientific community. In this research, different modes of transportation including pedestrians, taxis, buses, and bicycles are considered as a combination of two different transportation modes in Tehran’s densely populated area. With the aim of prioritizing travel options in order to achieve sustainable urban development, public transport users were first asked about their travel means preferences. Next, by obtaining the opinions of urban transportation experts of Tehran Municipality regarding sustainable development of urban transportation, a set of transportation options in this area were prioritized on the basis of some criteria optimized using the analytic hierarchy process (AHP) method. The results indicate that air pollution and noise pollution, with the score of 0.33 and 0.24, respectively, were the two most important criteria for choosing a trip mode in dense population areas according to the opinion of transportation and traffic experts of Tehran. The AHP analysis indicated that the use of combined bicycle-walk mode with a score of 0.282 is the most preferred option, while bus-bicycle mixed mode with a score of 0.116 is the least preferred. Survey data indicate that there is a significant difference between people’s general preferences in choosing their urban transportation options and the sustainable urban development approach.

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Electric vehicle routing problem: A systematic review and a new comprehensive model with nonlinear energy recharging and consumption

Cited by 167 publications

References 55 publications

Green Vehicle Routing Problem: State of the Art and Future Directions

Green Vehicle Routing Problem: State of the Art and Future Directions

Survey of charging management and infrastructure planning for electrified demand-responsive transport systems: Methodologies and recent developments

Mixed Transport Network Prioritization Based on Environmental Impact and Population Density

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