Ant colony algorithm for heterogonous green vehicle routing problem with split delivery

Logistic activities in urban areas are one of the leading reasons for greenhouse gas emissions. A mixed-integer linear programming model is proposed for heterogeneous green vehicle routing problem with split deliveries and time windows by considering different vehicle speeds and types. Random problems are generated and solved with the ant colony optimisation algorithm and compared with the CPLEX solver to validate the algorithm. Finally, a case problem is studied to determine managerial insights. The results suggest that vehicle capacity and type impose GHG emissions. If vehicle type is kept the same, emission released to the environment will decline with increasing vehicle capacity. Higher capacity vehicles generate less GHG emissions in scenarios with increasing demand and traffic intensity. However, small-capacity vehicles perform better in strict time windows. Finally, a reduction in emission with heterogeneous fleets is limited. Homogeneous fleets perform better and generate less emission in most of the scenarios.