Automobile Engineering

Statement of the Problem: The Vehicle Routing Problem (VRP) has various applications in real life. It illuminates in a wide field of transportation and  distribution, for example, transportation of people and things, movement service and garbage gathering.Subsequently, a proper choosing of vehicle routing has a broad impact factor to enhance the financial interests and fittingness of coordination’s planning. In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives: The proposed study attempt to find an optimal route results for VRP of any VRP using artificial intelligence methods. To achieve an optimal solution for VRP of VRP with the accompanying goals: To reduce the time consuming and distance for all routes.Which leads to the speedy transportation of customers to their locations, to reduce the transportation costs such as fuel utilization and additionally the vehicle upkeep costs, to implement the Capacitated Vehicle Routing Problem (CVRP) model for optimizing shuttle bus services. To implement the algorithm which can be used and applied for any problems in the like of VRP. Method: The Approach has been proposed based on two phases: firstly, find the shortest route for VRP to help anyorganization toreduce customer’s transportation costs by artificial intelligence methods is proposed to solve this problem as it is capable of solving many complex problems; secondly, identify The CVRP model is proposed for optimizing shuttle bus services. Finding: The findings outcome from this study have shown that: (1) A comprehensive listed of active artificial intelligence methods in solving vehicle routing problem for best transportation and distribution solutions; (2) Identified and established an evaluation criterion for artificial intelligence methods in solving vehicle routing problem for best transportation and distribution solutions; (3) Highlight the methods, based on hybrid crossover operation, for selecting the best route (4) artificial intelligence methods capable a shorter distance for routes. The proportion of reduction the distance for each route is relatively short, but the saving s in the distance becomes greater when calculating the total distances travelled by all buses daily or monthly. This applies also to the time factor thathas been reduced slightly based on the rate of reduction in the distances of the routes.