A key element of many distribution systems is the routing and scheduling of vehicles servicing a set of customers. A wide variety of exact and approximate algorithms have been proposed for solving the vehicle routing problems (VRP). Exact algorithms can only solve relatively small problems of VRP, which is classified as NP#Hard. Several approximate algorithms have proven successful in finding a feasible solution not necessarily optimum. Although different parts of the problem are stochastic in nature; yet, limited work relevant to the application of discrete event system simulation has addressed the problem. Presented here is optimization using simulation of VRP; where, a simplified problem has been developed in the ExtendSim TM simulation environment; where, ExtendSim TM evolutionary optimizer is used to minimize the total transportation cost of the problem. Results obtained from the model are very satisfactory. Further complexities of the problem are proposed for consideration in the future. Discrete event system simulation, optimization using simulation, vehicle routing problem. I. INTRODUCTION HE vehicle routing problem (VRP) is one of the most intensively studied problems in operations research, and this is due to its structural charm as well as practical relevance. Many papers have been devoted to the development of optimization[1#3]and approximation algorithms for vehicle routing and scheduling problems[4, 5]. This interest is due to the practical importance of effective and efficient methods for handling physical distribution situations as well as to the intriguing nature of the underlying combinatorial optimization models.The standard Vehicle Routing Problem (VRP)is an extension of the Travelling Salesman Problem (TSP), introducing demand at the customers and a fleet of vehicles, each having the same fixed capacity [6, 7]. Numerous methods have been proposed to solve the TSP. Finding the optimal route for a particular problem has not been practical for such problems when they contain many Nayera E. El#Gharably, B.Sc., is a Graduate Teaching Assistant at the Department of Industrial and Management Engineering; College of Engineering and Technology; Arab Academy for Science, Technology, and Maritime Transport; AbuKir Campus, P.O. Box 1029, Alexandria, Egypt (phone: +203#561#0755; fax: +203#562#2915; e#mail: nayera.elgharably@staff.aast.edu). Khaled S. El#Kilany, Ph. D., is a Professor of Industrial Engineering and Head of Department of Industrial and Management Engineering; College of Engineering and Technology; Arab Academy for Science, Technology, and Maritime Transport (e#mail: kkilany@aast.edu). Aziz E. El#Sayed, Ph. D., is a Professor of Industrial Engineering and Vice President for Education and Quality Assurance; Arab Academy for Science, Technology, and Maritime Transport (e#mail: azizezzat@aast.edu). points or require a solution to be found quickly. Computational time on the fastest computers for optimization methods has been too long for many practical problems. Cognitive, heuristic, or combination heuristic#optimization solution procedures have been good alternatives [8]. The aim of this work is threefold; to present a new mathematical formulation of the VRP problem that uses fewer decision variables, to show how to model the TSP problem as a discrete event simulation model, and to employ the developed simulation model in finding the optimum/near optimum solution of the problem. This paper is organized as follows: in Section II, the basic concepts of VRP and the solution techniques found in literature will be briefly discussed. In Section III, proposed problem formulations will be presented followed by the simulation model development and optimization using simulation in sections IV and V. Finally, in section VI, the conclusions drawn from this work are presented. II.LITERATURE REVIEW    The most addressed problem types in literature related to this work are: 1. The Travelling Salesman Problem The TSP is one of the simplest, but still NP#hard, routing problems. In this problem, a set of cities to be visited and a way to measure the distances between any 2 cities is given. The tour is not complete until the vehicle returns back to its starting point (depot). The objective is to find the shortest tour that visits all cities exactly once [8, 9]. 2. The m#Travelling Salesman Problem The #TSP is a generalization of the TSP that introduces more than one salesman. In the #TSP; citiesare given,  salesmen, and one depot. All cities should be visited exactly once by one of the salesmen. Each tour must start and end at the depot and salesmen are not allowed to be unassigned to cities [9]. 3. The Vehicle Routing Problem The VRP calls for the determination of a set of minimum cost routes to be performed by a fleet of vehicles to serve a given set of customers with known demands; where, each route originates and terminates at a single depot. Each customer must be assigned to only one vehicle and the total demand of all customers assigned to a vehicle does not exceed Optimization Using Simulation of the Vehicle Routing Problem Nayera E. El#Gharably, Khaled S. El#Kilany, and Aziz E. El#Sayed T World Academy of Science, Engineering and Technology International Journal of Industrial and Manufacturing Engineering Vol:7, No:6, 2013 1236 International Scholarly and Scientific Research & Innovation 7(6) 2013 ISNI:0000000091950263 Open Science Index, Industrial and Manufacturing Engineering Vol:7, No:6, 2013 publications.waset.org/15351/pdf