Datasets
Standard Dataset
Dataset for Green Location-routing Problem
- Citation Author(s):
- Submitted by:
- Dandan Su
- Last updated:
- Fri, 03/28/2025 - 06:19
- DOI:
- 10.21227/sv26-kb48
- License:
7 Views- Categories:
- Keywords:
Abstract
This dataset comprises ten adapted benchmark instances derived from the classical Prodhon dataset, along with one dataset based on a real-world logistics case study. The data are specifically designed to support the evaluation of a bi-objective Green Location Routing Problem (GLRP) model that aims to optimize both economic cost and environmental impact (carbon emissions). Each instance includes detailed information on depot locations and capacities, customer coordinates, demand values, time window settings, service durations, and depot fixed costs. These datasets facilitate reproducibility, comparative algorithm evaluation, and further research into sustainable logistics planning under different carbon tax policies. All files are provided in structured .txt format with accompanying documentation.
Adapted Benchmark Instances
Ten .txt files adapted from the classical Prodhon dataset. Each file represents one benchmark instance tailored to the bi-objective Green Location Routing Problem (GLRP), varying in customer size, depot number, and vehicle capacity.
Real-World Case Study Data
One .txt file based on actual enterprise data, used to analyze the effect of carbon tax in practical scenarios.
File Format and Structure:
All data files are in plain text (.txt) format, tab-delimited.
The first few rows (starting with d1, d2, etc.) represent candidate depots.
The remaining rows (starting with 1, 2, etc.) represent customer nodes.
Each row contains the following columns:
Column Description
No. Depot or customer ID (e.g., d1, d2, 1, 2...)
x, y Coordinates
Capacity / DemandCapacity (for depots) or demand (for customers)
ED / ES Earliest delivery (depots) or earliest service time (customers)
LD / LS Latest delivery (depots) or latest service time (customers)
Depot Cost / Service Duration Depot fixed cost or customer service time (in minutes)
Usage Notes:
Time windows are in 24-hour decimal format (e.g., 13.50 = 13:30).
All files can be read using standard text processing tools in Python, MATLAB, R, or Excel.
Researchers can use these datasets to benchmark multi-objective optimization algorithms, reproduce the GLRP experiments, or test new routing heuristics.
Please refer to the accompanying README.txt for a detailed summary of each instance and data structure.
