Building low CO2 solutions to the vehicle routing problem with Time Windows using an evolutionary algorithm

An evolutionary Multi-Objective Algorithm (MOA) is used to investigate the trade-off between CO₂ savings, distance and number of vehicles used in a typical vehicle routing problem with Time Windows (VRPTW). A problem set is derived containing three problems based on accurate geographical data which encapsulates the topology of streets as well as layouts and characteristics of junctions. This is combined with realistic speed-flow data associated with road-classes and a power-based instantaneous fuel consumption model to calculate CO₂ emissions, taking account of drive-cycles. Results obtained using a well-known MOA with twin objectives show that it is possible to save up to 10% CO₂, depending on the problem instance and ranking criterion used.