Dynamic Vehicle Routing Problem with Multiple Depots

Vehicle Routing Problems (VRPs) have been extensively studied and applied in many fields. Variants of VRPs have been proposed and appeared in researches for many decades. Dynamic Vehicle Routing Problem with Multiple Depots (D-MDVRP) extends the variation of VRPs to dynamism of customers by knowing the information of customers (both locations and due dates) at diverse times. An application of this problem can be found in food delivery services which have many service stores. The customer delivery orders are fulfilled by the scattered service stores where can be analogous to depots in D-MDVRP. In this example the information of all customer orders are not known at the same time depending on arrivals of customers. Thus the objective of this operation is to determine vehicle routing from service stores as well as dispatching time. This paper aims to develop the heuristic for D-MDVRP. The proposed heuristic comprises of two phases: route construction and vehicle dispatch. Routes are constructed by applying Nearest Neighbor Procedure (NNP) to cluster customers and select the proper depot, Sweeping and Reordering Procedures (SRP) to generate initial feasible routes, and Insertion Procedure (IP) to improve routing. Then the determination of dispatch is followed in the next phase. In order to deal with the dynamism, the dispatch time of each vehicle is determined by maximizing the waiting time to provide the opportunity to add more arriving customers in the future. An iterative process between two phases is adopted when a new customer enters the problem, and the vehicles are dispatched when the time becomes critical. From the computational study, the heuristic performs well on small size test problems in a shorter CPU time compared to the optimal solutions from CPLEX, and provides an overall average 8.36% Gap. For large size test problems, the heuristic is compared with static problems, and provides an overall average 3.48% Gap

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc