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

These files correspond to the supplementary material of the article The Electric Vehicle Travelling Salesman Problem on Digital Elevation Models for Traffic-Aware Urban Logistics. 

Code

• algorithm.py corresponds to an implementation of the algorithm developed in the paper to solve the EV-TSP  for the city of Madrid. It takes as input a list of nodes from the graph of Madrid city madrid_elevation_energy.pckl and the output consists of an ordered list of all the nodes representing the solution to the TSP.
• bellmanFord.py is a Python implementation of the Bellman-Ford algorithm. 
• evaluation.py is the script that offers the evaluation of the algorithm offered in Tables 1 and 2 in the paper.
• neuralNetworkTraining.py  is the script used to train and save the Neural Network model using the data generated by simulation.py.
• nn_model_predictor.py is a script where the model trained in neuralNetworkTraining.py can be used to generate predictions.
• simulation.py  is the script that simulated the routes through the months of October and November 2022 using the data in snapshots_2022.zip. It generates the routes in simulationOctober.csv and simulationNovember.csv
• twoOptNearestNeighnors.py is a Pyhton implementation of the 2-Opt algorithm that uses Nearest Neighbors to generate the initial tour.

Files

• Madrid{5,10,15}.pkl 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 (madrid_elevation_energy.pckl)   
• energy_estimation_full.h5 is a Keras model trained using nn_model_predictor.py to estimate the energy.
• scaler_full.pkl is the scaler needed to use the energy_estimation_full.h5 model.
• simulation{October, November}.pkl are the routes generated for each month using simulation.py.
• snapshots_2022.zip are the traffic data for the months of October and November 2022