A framework for solving real-time multi-objective VRP

One of the most important logistics problems in the field of transportation and distribution is the Vehicle Routing Problem (VRP). In general, VRP is concerned with the determination of a minimum-cost set of routes for distribution and pickup of goods for a fleet of vehicles, while satisfying given constraints. Today, most VRPs are set up with a single objective function, minimizing costs, ignoring the fact that most problems encountered in logistics are multi-objective in nature (maximizing customers’ satisfaction and so on), and that for both deterministic and stochastic VRPs, the solution is based on a pre-determined set of routes. Technological advancements make it possible to operate vehicles using real-time information. Since VRP is a NP-Hard problem, it cannot be solved to optimality using conventional methods; therefore, the paper presents a heuristic framework for solving the problem. In real-time dynamic problems, a solution is given based on known data, as time progresses, new data are added to the problem, and the initial solution has to be re-evaluated in order to suit the new data. This is usually done at pre-defined time intervals. If the time intervals are small enough, thus, at each time interval the amount of information added is limited. Therefore, the new solution will be similar to the previous one. Due to the fact that the result is a solution set, not a single solution, and one solution is to be selected within a short time window, it is necessary to automatically select a single solution. For that, a framework, based on traditional and evolutionary multi-objective optimization algorithms, which incorporate multi-criteria decision making methods, for solving real-time multi-objective vehicle routing problems is presented.