Comparison of Methods for Optimal Solutions to Vehicle Routing Problems

Abstract

The primary cost of logistics systems is transportation cost. Companies with poor transportation management result in higher transportation costs. The main factor of high transportation costs is employees choosing inefficient routes that result in a high total distance. In addition, as a result, delivery is also too late. Therefore, this research studies vehicle transportation routing in cases of certainty demand. A case study of distributor and delivery agriculture products that have one vehicle, and maximum loading capacity not exceeding 5,000 kg. The purpose of this research is to compare methods for finding the optimal solution with three methods; Genetic Algorithm (GA), Simulated Annealing (SA), and Hill-climbing Algorithm (HCA). The result shows that SA can solve the problem when the number of customers is 400, while HCA and GA can solve the problem when the number of customers is 350 and 200, respectively. Consequently, SA is the most effective method. Next are HCA and GA, respectively. GA is the method that gets the least distance and transportation time of the three methods when the number of customers is less than or equal to 200.