Improved genetic algorithm for variable fleet Vehicle Routing Problem with Soft Time Window

Vehicle routing problem with soft time windows (VRPSTW) is represented as a multi-objective optimization problem which both considering the number of vehicles and the total cost (distance). We simultaneously propose an improved genetic algorithm to solve this problem. In this algorithm, we solve the multi-objective optimization problem by variation of fitness function. We are not only increase the search ability of the algorithm but also satisfied the requirement of population diversity by using the improved crossover operator. We add the local search algorithm to make complete for the deficiency of the weak ability. The experiment result states that the algorithm is efficient for VRPSTW and can provide the useful support to make a better decision of transport problems.