Finding efficient vehicle routes is an important logistics problem which has been studied for several decades since at least the early 1960s[1]. When a company is able to reduce the length of its delivery route or is able to decrease its number of vehicles, it is able to provide better service to its customers, operate in a more efficient manner and possibly increase its market share. A typical vehicle routing problem includes simultaneously determining the routes for several vehicles from a center supply depot to a number of customers and returning to the depot without exceeding the capacity constraints of each vehicle. This problem is of economic importance to business because of the time and costs associated with providing a fleet of delivery vehicles to transport products to a set of geographically dispersed customers. In each of these instances, the problem typically involves finding the minimum cost of the combined routes for a number of vehicles in order to facilitate delivery from a supply location to a number of customer locations. Since cost is closely associated with distance, a company might attempt to find the minimum distance traveled by a number of vehicles in order to satisfy its customer demand. In doing so, the firm attempts to minimize costs while increasing or at least maintaining an expected level of customer service. This typical vehicle routing problem is not quite fit to real-world industrial applications mainly due to various business relationships between a supplier, transporters, and customers. In general, most of large manufacturing companies do not have their own vehicle for delivering their products to customers. For those manufactures, it is more common to sign contracts with multiple transportation companies and the contracts includes transportation costs and size of the service that defines a amount of products to be delivered by each of the transporters in a contracted period. In this business relationship, the manufacturer is a customer for the transporters and the transporters are set to be in a naturally competitive environment. To minimize transportation cost and attain appropriate customer service level, the manufacturer need to decide optimal “vehicle dispatching” solution under the given transportation services and resources provided by the transporters. In this study, an optimal vehicle dispatching system with mathematical programming models are developed. There are three types of key decision variables, which types of vehicles are to be used, how many numbers of the vehicles are required, which orders are to be loaded on the vehicles, to obtain minimum transportation cost with appropriate service levels. Due to the realistic constraints, it is necessary to formulate the model with multi-objective optimization problem. One of the key contributions of this research is handling the multi-objective with subtle scaling technique to obtain a reliable solution in limited time for industrial application. Efficiency of the mathematical model is tested for a real-world problem.

[1] G. Clark, JW Wright, “Scheduling of vehicles from a central depot to a number of delivery points.” Oper Res, vol. 12, pp 568-81, 1964.

Key Words —Vehicle Dispatching, Multi-Objective, Optimization, Industrial Applications.