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

Abstract

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.

Multi-Objective Routing Optimization in Electric and Flying Vehicles: A Genetic Algorithm Perspective

“…The chosen representation directly impacts the algorithm's ability to explore diverse solutions within the search space. In message routing, efficient encoding is necessary to capture the sequence of waypoints, charging stations, and flight paths [27,28].…”

Optimizing Electric Truck Routing and Charging with Soft Time Windows Using Vehicle-to-Grid Technology

“…In a recent comprehensive review paper, Sabet and Farooq [17] conducted a thorough evaluation of multiple adaptations and specifc instances of the green vehicle routing problem (GVRP) aimed at resolving challenges concerning charging, pickup, delivery, and energy usage. Teir analysis highlighted the prevalence of metaheuristic techniques in most studies, with limited attention given to emerging machine learning approaches.…”

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

“…These studies also investigate innovative solutions, infrastructure setup, staffing, and fleet design in recent years [5,7]. The application of computational intelligence [8], new solution methods [9,10], and the environmental side effects of VRP and LMD [11] are also widely discussed in the literature. Optimization models to employ various types of vehicle in LMD are discussed in [5,12,13].…”