M2P 2023

Machine Learning Assists to Solve Green Vehicle Routing Problem

  • Shahbazian, Reza (University of Calabria)
  • Guerriero, Francesca (University of Calabria)

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The goal of the green vehicle routing problem (GVRP), a variant of the traditional vehicle routing problem (VRP), is to reduce the environmental impact of a fleet's travels by using fuel-efficiency, such as electric vehicles (EVs). The goal of the electric vehicle routing problem (EVRP), a type of GVRP, is to determine the best routes for a fleet of EVs while taking into account variables such as battery range and charging station accessibility. When routing the cars, GVRP strives to reduce greenhouse gas emissions by using alternative fuel vehicles (AFVs). Although there is a link in the literature assessment between GVRP and current fossil fuel vehicles, this is not something that is expected for GVRP in the future. The GVRP's future is likely to be influenced several trends and events as explained in the following: • Vehicles that run on electricity: New algorithms and GVRP methodologies are expected to emerge as EV adoption grows in response to the EVs' unique set of limitations and goals, which differ from those of conventional fuel-powered vehicles in areas such as battery range and charging station accessibility. • Connected and autonomous vehicles (CAVs): As CAVs develop, the GVRP will undoubtedly face new challenges as well as opportunities. On the one hand, by interacting with traffic control systems and each other, CAVs may improve route efficiency. On the other hand, because of the increasing data needs and complexity of CAVs, more complex algorithms and GVRP techniques may be required. • Smart cities: The rise of smart cities, in which infrastructure and services are optimized through the use of sensors and real-time data, is likely to open up new opportunities for the GVRP. Weather and traffic data, for example, can be used to optimize routes in real time and reduce environmental impact. • Climate change: As the importance of reducing emissions and the environmental impact of transportation grows, climate change is expected to increase the significance of the GVRP. • Big Data and Machine Learning (ML): As data sets grow and ML advances, GVRP will be strengthened because better decision-making will be enabled by more accurate and real-time data, which will also improve prediction and control of energy usage. These trends and advancements are expected to inspire further research and development of new algorithms and methodologies for the GVRP, with the goal of finding more effective and sustainable routes for