Developing an efficient dispatching strategy to support commercial fleet electrification
Data files
Jul 05, 2023 version files 345.47 KB
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BETVRPB1_dist.csv
20.29 KB
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BETVRPB1_time.csv
17.85 KB
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BETVRPB1.csv
4.58 KB
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BETVRPB2_dist.csv
30.24 KB
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BETVRPB2_time.csv
26.65 KB
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BETVRPB2.csv
5.54 KB
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BETVRPB3_dist.csv
46.28 KB
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BETVRPB3_time.csv
39.90 KB
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BETVRPB3.csv
6.77 KB
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BETVRPB4_dist.csv
72.60 KB
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BETVRPB4_time.csv
64.21 KB
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BETVRPB4.csv
8.48 KB
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README.md
2.08 KB
Jan 02, 2024 version files 339.96 KB
Abstract
This is a real-world dataset in a full-service supply chain company to evaluate the performance of our proposed battery electric truck dispatching strategy. We generated four instances ranging from 47 to 90 customers based on the real-world dataset, the typical one-day historical movements of a heavy-duty diesel truck fleet that operated in the Riverside and San Bernardino County regions of California.
README
Developing an Efficient Dispatching Strategy to Support Commercial Fleet Electrification [dataset]
The generated four fleet operation instances ranging from 47 to 90 customers are based on a real-world dataset, the typical one-day historical movements of a heavy-duty diesel truck fleet that operated in the Riverside and San Bernardino County regions of California.
Description of the data and file structure
There are three types of CSV files: problem instance (BETVRPB), related distance matrix (BETVRPB_dist), and time matrix (BETVRPB_time). The distance matrix and time matrix files are used for the calculation. The detailed information is illustrated in the following.
For the problem instances, the generated battery electric truck (vehicle) routing problem with backhauls (denoted BETVRPB) instances in our case study are described in the CSV files, i.e., BETVRPB1-BETVRPB4. It contains delivery ID, tractor ID, delivery types (i.e., pickups and deliveries), service time duration, required demands, and city name. That information is shown in the columns of the file BETVRPB from left to right, respectively. The depot time window section is shown in the last row of each problem instance.
For each dispatching instance, the distance [in meter] and travel duration [in second] matrices are generated by OpenRouteService (openrouteservice.org) for the truck routes between node-to-node locations, which can be used to estimate the trip energy consumption.
There is an example illustrating how to use the generated instances. Take the problem instance BETVRPB1 as an example. It contains 47 nodes, where the depot ID is 0, and others are customers (from 1 to 46). The related distance and trip time information is illustrated in files 'BETVRPB1_dist' and 'BETVRPB1_time' for calculation steps. For example, the truck travel distance from the depot (0) to the customer ID1 is the element (0,1) in the distance matrix file (i.e., BETVRPB1_dist). The calculation of travel time is similar to the travel distance.