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7 results for “Vehicle Routing”
Vehicle Routing Problem with Drones Instances
<p>The following instances are originally presented in the paper <em>An Adaptive Large Neighborhood Search Metaheuristic for the Vehicle Routing Problem with Drones</em>, written by David Sacramento Lechado, David Pisinger and Stefan Røpke, and published in <em>Transportation Research Part C: Emerging Technologies</em>.</p> <p>The data correspond to 112 instances corresponding to different scenarios for the Vehicle Routing Problem with Drones. Each instance is named <strong>n.m.t</strong>, where <strong>n</strong> is the number of customers in the scenario, <strong>m</strong> is the dimension of the grid, and <strong>t</strong> is the generic name of the scenario. Moreover, the data additionally contains 10 clustered instances, named <strong>n.m.c.t</strong>, where <strong>c</strong> refers to the cluster-feature of the instance, 100 instances for the sensitivity analysis, named <strong>n.m.s.t</strong>, where <strong>s</strong> stands for sensitivity, and 375 instances for the experiments with drone savings as function of grid size, named <strong>n.m.g.t</strong>.</p> <p>The first line of each instance file indicates the number of customers in the specific instance. The second line is the header for the characteristics of each customer, where it is written <em>Coordinate X, Coordinate Y</em> and <em>Demand. </em>The following provides the previous information for each customer in the instance.</p> <p>Finally, there is an extra file, named <strong>RouteData,</strong> which provides information about the value of the parameters in the main configuration of the problem. These parameters are:</p> <ul> <li><strong>TruckSpeed: </strong>Speed in mpm (miles per minute) of the trucks.</li> <li><strong>DroneSpeed: </strong>Speed in mpm of the drones.</li> <li><strong>TruckCapacityWithDrones: </strong>Maximum capacity of the trucks for the drone-truck scenario.</li> <li><strong>TruckCapacityWithoutDrones: </strong>Maximum capacity of the trucks for the truck-only scenario.</li> <li><strong>DroneCapacity:</strong> Maximum allowed weight a drone can carry.</li> <li><strong>ServiceTimeTruck:</strong> Required service time for a truck to service a customer.</li> <li><strong>ServiceTimeDrone:</strong> Required service time for a drone to service a customer.</li> <li><strong>Endurance: </strong>Maximum flight endurance of the battery of the drone.</li> <li><strong>MaximumDriveTime: </strong>Maximum duration time of the routes.</li> <li><strong>LaunchTime: </strong>Required time for launching a drone.</li> <li><strong>RecoveryTime: </strong>Required time for recovering a drone.</li> <li><strong>CostFactor:</strong> Corresponding parameter for computing the cost for a truck for traversing arc (i,j), given by fuel price (euro/liter), consumption rate (liter/km) and miles converter (km/miles).</li> <li><strong>DroneFactor: </strong>Corresponding parameter for computing the cost for a drone for traversing arc (i,j) with respect to the truck cost.</li> </ul> <p> </p>
Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem
<p>Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem. <br> The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.</p>
Constrained Fitness Landscape Analysis of Vehicle Routing Problems
<p>The repository is a set of Jupyter notebooks and datasets used in the article titled: “Constrained Fitness Landscape Analysis for Capacitated Vehicle routing problems.” The main objective is to give the tools for reproducing the results shown in the paper.</p>
About prediction of vehicle energy consumption for eco-routing: simulation results
<p>Supplementary materials, experiment results, processing scripts</p>
Autoconfig: Vehicle Routing Problem with Occasional Drivers
<p>Autoconfig: Vehicle Routing Problem with Occasional Drivers</p> <p>See https://github.com/rmartinsanta/ac-BMSSC for full details.</p> <div> <div> <div> </div> <div>Authors of the original paper:</div> <div>Raúl (Martín-Santamaría) </div> </div> </div> <div> <div> <div>Ana Dolores (López Sánchez) </div> </div> </div> <div> <div> <div>María Luisa (Delgado Jalón) </div> </div> </div> <div> <div>José Manuel (Colmenar Verdugo) </div> </div>
Neural Column Generation for Capacitated Vehicle Routing
<p>The dataset used in the experiments of the paper "Neural Column Generation for Capacitated Vehicle Routing"</p>
Supplementary material for publication "The combined second-echelon vehicle routing problem - Integrating last-mile deliveries into public transport"
<p>Supplementary material for publication "The combined second-echelon vehicle routing problem - Integrating last-mile deliveries into public transport".</p> <p> </p> <p>Includes bus times (provided by the data set goettingen from the scientific software toolbox LinTim (https://lintim.net/)), customer nodes, and distances and results for the algorithms for different vehicle speeds, capacities, number of second-echelon vehicles and number of customers.</p>
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