A metaheuristic for the time-dependent pollution-routing problem

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver's wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm.