DOI: 10.5061/dryad.6hdr7srbv
title: Routing short-haul trucks under the uncertainties of travel time and service time

description: This dataset supports research on optimizing battery electric vehicle
 (BEV) fleet dispatching in last-mile freight logistics under uncertainty.
 It accompanies the study on the Electric Vehicle Routing Problem with
 Backhauls and Time Windows under Travel Time and Service Time Uncertainty
 (EVRPBTW-USUT), which extends previous research by incorporating a
 backhauling strategy and modeling uncertainty in travel and customer
 service times. The dataset consists of 60 benchmark instances, derived
 from the well-known EVRPTW dataset, with varying customer sizes and
 backhaul proportions, enabling robust evaluations of routing strategies.
 Additionally, a real-world dispatching dataset from a full-service supply
 chain company in San Bernardino County, California, is included to
 validate the approach in practical applications. Each instance is provided
 in CSV format, with detailed solutions recorded in Excel files. These
 datasets support the development and benchmarking of optimization
 algorithms, particularly for robust vehicle routing and sustainable urban
 freight logistics.
description: # Routing short-haul trucks under the uncertainties of travel time and
 service time Author: Dongbo Peng
 ([dpeng017@ucr.edu](mailto:dpeng017@ucr.edu)) #### 1.1 Vehicle Routing
 Problem with Backhauls Benchmark Dataset Description Folder:
 Section_3_1_BKS_dataset_and_results The file "TV_33_instances"
 contains a total of 33 problem instances in CSV format, original
 introduced by (Toth & Vigo, 1997), with customer sizes ranging from 21
 to 100. Each instance is named according to the number of customers and
 the proportion of linehaul customers. For example, the file
 "eil22_50.csv" represents an instance with 22 nodes, where 50%
 of the customers are linehaul customers. Specifically, in each instance,
 the columns "node_id", "type", "x",
 "y", "demand", "Q", "k",
 "L", and "B" represent the node ID, service type
 (where -1 denotes the depot node, 0 denotes a linehaul customer, and 1
 represents a backhaul customer), the x and y coordinates of the node, the
 customer demand, vehicle capacity (Q), the vehicle index (k), and the
 number of linehaul (L) and backhaul (B) customers, respectively. All the
 detailed results/solutions are recorded in the Excel file,
 "Benchmark_TV_results.xlsx." The results table summarizes the
 performance of the proposed algorithm across multiple problem instances.
 Each row corresponds to a single instance and includes benchmark
 comparisons and solution details. The columns are described as follows:
 "Instance": The name of the problem instance, indicating the
 dataset used (e.g., eil22_50 refers to 22 customers with 50% linehaul
 customers). "BKS": The Best Known Solution (BKS) value from the
 literature or benchmark. "Best": The best solution value
 obtained by the proposed algorithm. "DEV(%)": The percentage
 deviation of the obtained best solution from the BKS, calculated as DEV(%)
 = (Our_solution - BKS) / BKS ×100. t(s): The computational time in seconds
 required to obtain the best solution. "BEST_Results": The best
 route solution(s) found by the algorithm, represented as a list of vehicle
 routes. Each sublist contains a sequence of node IDs visited by a vehicle,
 with 0 indicating the depot. The last row, "Average", shows the
 average value for each column. #### 1.2 EVRPBTW-USUT Benchmark Dataset
 Description Folder: Section_3_2_EVRPBTW_benchmark To examine the proposed
 electric vehicle routing problem with backhauls and time windows under
 travel time and service time uncertainty (EVRPBTW-USUT), we introduce a
 new EVRPBTW benchmark dataset based on the well-known EVRPTW benchmark
 instances introduced by Schneider et al. (Schneider et al., 2014). Our
 EVRPBTW benchmark dataset incorporates a backhaul strategy for a battery
 electric vehicle (BEV) fleet, reflecting a practical application in urban
 logistics and supply chain services. Customers are divided into two
 groups: linehaul customers (L), who require deliveries, and backhaul
 customers (L), who require pickups. We selected 10 instances from the
 EVRPTW benchmark dataset, where the customers are randomly distributed in
 the hypothesis scenario. For each instance, similar to (Toth & Vigo,
 1997), we generate two EVRPBTW instances based on one EVRPTW dataset, each
 corresponding to an approximate linehaul percentage of 66% and 75%,
 respectively. Backhaul customers are grouped in sets of two, three, or
 four. Other aspects, such as customer locations, time windows, recharging
 stations, and EV characteristics, remain unchanged. Therefore, for each
 EVRPTW benchmark dataset, we can generate two new EVRPBTW instances. For
 example, the instance named “r201_C25B3” represents a case with 25
 customers, where 13 are linehaul customers and 12 are backhaul customers,
 accounting for approximately 66% backhaul customers. This instance
 includes 21 recharging stations. In total, 60 instances are generated. The
 full set of instances can be found in the problem instance files. In each
 EVRPBTW instance, it contains the following features: {'ID':
 customer ID, 'Type': service type ('D', 'S',
 'L', 'B' denote depot, charging station, linehaul
 customer and backhaul customer, respectively), 'x': x
 coordinate, 'y': y coordinate, 'demand': customer
 demand, 'ReadyTime': earliest service start time,
 'DueTime': latest service start time, 'ServiceTime':
 nominal service time}.ßß For the problem instance parameters, we follow
 the original problem assumption in (Schneider et al., 2014). For example:
 Q: Vehicle fuel tank capacity C: Vehicle load capacity r: fuel consumption
 rate g: inverse refueling rate v: average Velocity #### 1.3 Structure of
 the benchmark datasets. Folder: Section_3_3_Real_case_dataset_and_results
 The compressed ZIP file "Section_3_1_BKS_dataset_with_results"
 contains the benchmark dataset obtained from (Toth & Vigo, 1997) along
 with our solutions. The TV89 instance set comprises 36 problem instances
 with customer sizes ranging from 21 to 100. Detailed solutions are
 documented in Excel files. The new EVRPTWB benchmark dataset contains 60
 instances, with 20 instances including 25 customers, 20 instances
 including 50 customers, and 20 instances including 100 customers. The
 instances are provided as CSV files. We store the problem instances and
 detailed solutions in the compressed ZIP file, named
 "Section_3_2_EVRPBTW_benchmark_instances". The structure of the
 compressed ZIP file is shown as follow: \--&gt;
 Section_3_2_EVRPBTW_benchmark \--&gt; C25B3 \--&gt; r201_C25B3.csv \--&gt;
 r202_C25B3.csv \--&gt; ... \--&gt; C25B4 \--&gt; C50B3 \--&gt; C50B4
 \--&gt; C100B3 \--&gt; C100B4 The real-world dispatching data is stored in
 a ZIP file, which includes a CSV file for an instance with 47 customers
 and an Excel file containing detailed solutions. There are three types of
 CSV files: Problem instance: Section3_real_case_data.csv Distance matrix:
 real_case_distance_matrix.csv Time matrix: real_case_time_matrix.csv This
 Excel file "Section_3_3_Real_case_solutions.xlsx" provides a
 comparison of two vehicle routing solutions generated for the same
 transportation network. Each solution consists of a set of routes
 originating and ending at a central depot (node 0). The routing results
 are listed under: Det_sol: The routing solution obtained from a
 deterministic model, where travel times, service times, and other
 parameters are assumed to be known and fixed. Rob_sol: The routing
 solution obtained from a robust optimization model, which accounts for
 uncertainty in travel and service times to enhance solution reliability.
 Each solution is represented as a list of routes, with each route defined
 as a sequence of node IDs. All routes start and end at node 0, which
 denotes the depot. References: Schneider, M., Stenger, A., & Goeke, D.
 (2014). The electric vehicle-routing problem with time windows and
 recharging stations. Transportation Science, 48(4), 500–520. Toth, P.,
 & Vigo, D. (1997). An Exact Algorithm for the Vehicle Routing Problem
 with Backhauls. Transportation Science, 31(4), 372–385.
 [https://doi.org/10.1287/trsc.31.4.372](https://doi.org/10.1287/trsc.31.4.372)