Development a Visual Routing Software for the Solution of Vehicle Routing
Problems with Genetic Algorithm Modeling distribution networks and planning efficient routing alternatives that are provided by the solution of Vehicle Routing Problem (VRP) frequently appears in literature. VRP is determining the route sets which will minimize total transportation cost by ensuring all operational constraints for distribution of demands to geographically dispersed customers by using certain number of vehicles from a common depot or depots.
In this thesis, a visual vehicle routing software that can be used in real-life applications was developed to provide a platform for solving routing problems. A genetic algorithm was implemented for route solutions. The genetic algorithm was improved by applying the 2-opt local search heuristic as hybrid. With this designed algorithm, the visual vehicle routing software can be used to search route solutions for different types of problems: Capacity Constrained Vehicle Routing Problem (CVRP), Distance Constrained Vehicle Routing Problem (DVRP), Travelling Salesman Problem (TSP), Multiple Travelling Salesman Problem (MTSP).
A visual routing software that was designed to produce solutions to different types of routing problems is not available. The main contribution of the thesis is to close this gap. The goal of the study is to design a visual routing software that provides ease of use and implements planning of feasible routes according to operational constraints existing in real-life problems.
