Data supporting 'Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles: An assessment of potential for change'
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Bray, G., & Cebon, D. (2022). Data supporting 'Selection of vehicle size and extent of multi-drop deliveries for autonomous goods vehicles: An assessment of potential for change' [Dataset]. https://doi.org/10.17863/CAM.85777
Folder contains all sets of VRP analyses described in the article. Analyses include full datasets, radial and random data subsets for 'Customer Deliveries' and 'Intermediate Distribution' case studies. Each folder consists of the following files: DistMatrix.txt: This is a matrix of distances between each destination in the analysis in kilometres FVOT.txt: This a small table listing the different values of FVOT (Freight Value of Time) LocDemand.txt: This is a table listing the reference numbers of the destinations in the dataset, the quantity of cargo required to be delivered to each destination and the coordinates of the destination. To preserve commercial confidentiality, coordinates have been removed for the uploaded datasets. In the 'Intermediate Distribution' cases, cargo loads are created in multiples of 12 cages with dummy location numbers used to differentiate between each multiple of 12 cages. VehParams.txt: This is a table of vehicle parameters including hourly driver and vehicle cost (in £/hr), mass capacity of the vehicle (in tonnes), volume capacity (in units of cargo, e.g. crates for LGVs and cages for rigid/tractor-trailer), mass per unit volume, fuel consumption when the vehicle is empty and full respectively. LWFMVRP.py: This is the Python program which runs the optimization analysis using Gurobi commercial solver. Output.xlsx: This is the excel output of the analysis and includes a summary as well as output matrices for the best solution discovered by the analysis.
Python Microsoft Excel
autonomous freight, autonomous goods vehicles, autonomous vehicles, Vehicle Routing Problem
Related Item: https://doi.org/10.17863/CAM.87207
Publication Reference: https://doi.org/10.1016/j.tre.2022.102806
This record's DOI: https://doi.org/10.17863/CAM.85777
Attribution 4.0 International (CC BY 4.0)
Licence URL: https://creativecommons.org/licenses/by/4.0/