A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations

This study addresses the challenge of efficiently routing both electric and conventional trucks to minimize transportation costs and reduce greenhouse gas emissions. To achieve this, we developed a novel mathematical model focused on a transportation network comprising depots, customer locations, and charging stations. The primary objectives were to minimize transportation costs and greenhouse gas emissions. To tackle this complex problem, we integrated three multi-objective solution methods with Adaptive Large Neighborhood Search. Real-world data from locations in the Greater Toronto Area and Ontario, Canada, were utilized to generate instances for analysis. The findings revealed that increasing charging power and doubling the number of stations could significantly decrease transportation costs. Interestingly, a slight rise in transportation costs corresponded to a noteworthy reduction in greenhouse gas emissions. For example, upgrading charging power from 90 kW to 350 kW yielded a 5% reduction in transportation costs, while doubling the number of stations reduced costs by 2%. Moreover, even a minor increase in transportation costs (less than 3%) led to a substantial decrease (over 18%) in greenhouse gas emissions. This research provides valuable insights for delivery companies seeking to optimize their routing strategies while addressing environmental concerns.Amiri, A., Hassanzadeh Amin, S., & Zolfagharinia, H. (2023). A bi-objective green vehicle routing problem with a mixed fleet of conventional and electric trucks: Considering charging power and density of stations (external link) . Expert Systems with Applications, Volume 213, Part C, 119228.