The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics Supplementary Material

<p>These files correspond to the supplementary material of the article&nbsp;<em>The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics</em>.&nbsp;</p> <p><strong>Code</strong></p> <ul> <li><strong>algorithm.py</strong>&nbsp;corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP &nbsp;for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city <strong>madrid_elevation_energy.pckl</strong>&nbsp;and the output consists of an ordered list of all the nodes representing the solution to the TSP.</li> <li><strong>bellmanFord.py</strong>&nbsp;is a Python implementation of the Bellman-Ford algorithm.&nbsp;</li> <li><strong>evaluation.py</strong>&nbsp;is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.</li> <li><strong>neuralNetworkTraining.py</strong>&nbsp;&nbsp;is the script used to train and save the Neural Network model using the data generated by <strong>simulation.py</strong>.</li> <li><strong>nn_model_predictor.py</strong>&nbsp;is a script where the model trained in&nbsp;<strong>neuralNetworkTraining.py</strong>&nbsp;can be used to generate predictions.</li> <li><strong>simulation.py</strong>&nbsp; is the script that simulated the routes through the months of October and November 2022 using the data in <strong>snapshots_2022.zip</strong>. It generates the routes in <strong>simulationOctober.csv</strong>&nbsp;and <strong>simulationNovember.csv</strong></li> <li><strong>twoOptNearestNeighnors.py</strong>&nbsp;is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.</li> </ul> <p><strong>Files</strong></p> <ul> <li><strong>Madrid{5,10,15}.pkl</strong>&nbsp;are the test instances for the city of Madrid. Correspond to Python list of list. Each list is a set of stops to visit in the city graph of Madrid (<strong>madrid_elevation_energy.pckl</strong>) &nbsp;&nbsp;</li> <li><strong>energy_estimation_full.h5</strong>&nbsp;is a Keras model trained using <strong>nn_model_predictor.py</strong>&nbsp;to estimate the energy.</li> <li><strong>scaler_full.pkl</strong>&nbsp;is the scaler needed to use the <strong>energy_estimation_full.h5</strong>&nbsp;model.</li> <li><strong>simulation{October, November}.pkl</strong>&nbsp;are the routes generated for each month using <strong>simulation.py</strong>.</li> <li><strong>snapshots_2022.zip</strong>&nbsp;are the traffic data for the months of October and November 2022</li> </ul>