An Adaptation of the RecordtoRecord Travel Algorithm for the Cumulative Capacitated Vehicle Routing Problem The cumulative capacitated vehicle routing problem (CCVRP) is a CVRP that aims to minimize the cumulative of vehicle arrival times at every customer. This problem is suitable for the customercentric or servicecentric problem to increase the service level at customers. In the routing problem, there are many metaheuristic methods applied to solve the problem. Previous studies show that the record to record travel algorithm (RRT) has been proven to have excellent and competitive results when solving the VRP. In this paper, an RRT based algorithm is developed to address CCVRP. Several local searches (uphill and downhill moves), two general structures, and parameter values are adopted in the proposed algorithm. The dataset from previous studies is used to test the algorithm. The result obtained shows that the total cost and CPU time are competitive when compared with the published result.

Routing a Fleet of Automated Vehicles in a Capacitated Transportation Network Routing of a fleet of automated unitoccupancy vehicles in a capacitated transportation network is an emerging problem that needs to be addressed to realize largescale automated transportation systems. We adopt an existing networkflowbased model for the problem and present a new reformulation based on DantzigWolfe decomposition. This reformulation allows us to apply the column generation solution technique which, in turn, enables us to solve largescale problem instances with tens of thousands of requests on networks with thousands of links. We empirically compare our method to the stateoftheart approach on several standard benchmark instances and find that the computational time of our solution approach scales qualitatively better in all tested problem instance parameters: namely, in the size of the transportation network, in the magnitude of demand intensity, and in the number of demand flows.