Software framework for vehicle routing problem with hybrid metaheuristic algorithms

The objective of vehicle routing problem (VRP) is to design a set of vehicle routes in which a fixed fleet of delivery vehicles from one or several depots to a number of customers have to be set with some constraints. To this date in the literature, many instances of VRP model have been introduced and applied for various types of scheduling problems. However, when implemented in a real life application, the VRP models proved to be very complex and time consuming, especially in the development phase. It is due to the fact that there are technical hurdles to overcome such as the steep learning curve, the diversity and complexity of the algorithms. This paper presents a generalize software framework for an effective development of VRP models. The software framework presented here is hybridized algorithm of two metaheuristics namely as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The hybrid algorithm is used to optimize the best route for the vehicles that also incorporates a mechanism to trigger swarm condition for PSO algorithm. In order to test the functionality of the software framework, the applications of Pickup and Delivery Problem with Time Windows (PDPTW) and Vehicle Routing Problem with Time Windows (VRPTW) are developed based on the software framework. Experiments have been carried out by running the hybrid PSO with the VRPTW and PDPTW benchmark data set. The results indicate that the algorithm is able to produce significant improvement mostly to the PDPTW.