Optimization of Pick up and Delivery Problem of Postal Service between the Centers by Capacitated Vehicles based on Metahuristic Algorithms

The development of effective decision support tools that can be adopted in the transportation industry is vital since it can lead to substantial cost reduction and efficient resource consumption. However, vehicles moving on our roads contribute to congestion, noise¡ pollution, and accidents. So route planning and transport management, using optimization tools, can help reduce transport costs by cutting mileage and improving driver and vehicle usage. In addition, it can improve customer service, cut carbon emissions, improve strategic decision making and reduce administration costs.
Due to the simultaneous pick-up and delivery postal service and delivery time importance of those parcels, this study focuses on the pick-up and delivery problems. The pick-up and delivery problems are important types of vehicle routing problem (VRP). VRP is the core of scientific research on the distribution and transport of people and goods. Unlike the classical VRP, in which all customers require the same services, in the pick-up and delivery problem basic it is considered that two different types of services can be found in one place, in fact there's a pick up or delivery. PDP has several applications in the transportation of pick-up and delivery parcel post. The purpose of this research is to find the most optimal route to transport postal service. It is performed by imposing a series of conditions to the pick-up and delivery problems using meta-heuristic algorithms for the simulation data. It is followed by brief explanation of present metaheuristic algorithms including bee colony algorithm and genetic algorithms and their features. Finally the results of the algorithms are compared on the basis of the accuracy, repeatability, speed of convergence. It is necessary to note that the results are not ideal, but the best case is considered. The results showed the performance of the bee algorithm are better than genetic. Based on the results obtained in each run, genetic algorithms and Bee were 84% and 93% are possible to achieve the best solution.