Evolutionary algorithm for a Green vehicle routing problem with multiple trips

This paper deals with a variant of vehicle routing problem where vehicles are allowed to take more than one route during the working day. The depreciation of the vehicle may be a bad investment for green transportation because it could generate more emissions. Hence, it is necessary to satisfy green transportation requirements by reducing the CO2 emissions from road transportation. The objective is to optimize the amount of greenhouse gas emissions. A restricted fleet size is used to serve demands, so the vehicles could exceed the time horizon. It is subject also to minimize the maximum overtime to find feasible solutions. A mathematical model has been proposed for the Green Vehicle Routing Problem with multiple trips (GVRPM). An evolutionary algorithm has been developed to solve it by combining a genetic algorithm with a local search procedure. The effectiveness of our approach is tested on a set of benchmarks. Comparing with existing algorithm, our approach shows competitive performance and contributes many new best solutions.