10.13130/RD_UNIMI/JEA5XXBEZZI, DARIODARIOBEZZI0000-0003-0339-217XUniversità degli studi di MilanoElectric Vehicle Routing Problem with Multiple Recharge Technologies (EVRP-MRT) DatasetEVRP-MRTUNIMI Dataverse2021Computer and Information ScienceEVRPBEZZI, DARIODARIOBEZZIUniversità degli studi di Milano2020-12-012021-02-174581428625458952863545743286204567528610436672863145315286314535328601446312864844871286504550228602416041887139986188824006418872401151888639756188714003418854402071888340111189033994318913402611886558461861904182618473339486907470583035934971571440891164244417225126580088623642800184884723230380386691448335897821944228276973451268995956286223594288623059484862405806886171595588617558643861755903986198579088623039422164104953739123386351445834114690246277331513387968953952616418447663316434579690242556184743772082194569230366400301642645969303633550382164018516421461873036435837821739850164184555130351480833608635727821440522164193482569037469161674232616743016157214761674954717737135357404959878409636789596157588459882541598813255368125759986914554842916188103955588722599919116187947661880831618100607120265169467582323246910555751489278622154666786353998849115962811404634605115640244955737278567984945996227810250011401037041159111847122110118911029989611409860611021011001121995451121643094497403765564169448512713387327665456592356653733866788651254838295664294226060929565803494695085677310965754225358640475487227465554993375557833876469249672479514567802966405046850519279image/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xmlimage/pngtext/xml1.0CC0 WaiverIn this repository, the benchmark dataset for the Electric Vehicle Routing Problem with Multiple Recharge Technologies is introduced (EVRP-MRT). The whole benchmark is composed by 66 instances comprised by 5 to 30 customers and 3 to 9 recharge stations. All instances are solved to proven optimality. Instances and optimal solutions are presented in xml format, compliant with the VRP-rep specifications. The <a href="https://vrp-rep.github.io/mapper/">VRP-rep instance mapper</a> was used to draw the graph of each instance (and its solution) in png format.<h2>Dataset A</h2> This dataset was derived from the Solomon dataset, by relaxing the time windows constraints: instances have up to 15 customers (the last part of the name indicates the size of each instance) and 5 stations, with a single technology. Some of these instances are very small and not challenging; they serve mainly to make a comparison between the results of similar problems. Instances in dataset A are splitted in three classes: C (clustered), R (random) and RC (random-clustered), according to distances between customers. This dataset contains 12 instances for each class. <h2>Dataset B</h2> Instances in this dataset have 10 customers, up to 5 vehicles, up to 9 stations and 3 technologies. This dataset contains 20 instances. <h2>Dataset C</h2> This dataset was adapted from the Solomon dataset: all instances have 30 customers, 7 vehicles, 5 stations and 3 technologies. In this dataset customer locations are clustered. This dataset contains 10 instances.