Get started
Work

Parameter Tuning of GA for the CVRP Using Multi-Phase DoE

Year: 2026
Type: article
Abstract: This record contains the raw data, benchmark reference data, source code, statistical analysis scripts, and statistical test results required to reproduce the findings reported in the study “Parameter Tuning of Genetic Algorithm for the Capacitated Vehicle Routing Problem Using Multi-Phase Design of Experiments”. The record includes: (1) machine-readable raw experimental data for the three phases of the study, (2) benchmark reference data used to configure the CVRP instances, (3) C++ source code implementing the genetic algorithm variants for the Capacitated Vehicle Routing Problem (CVRP), (4) Python scripts used to perform the statistical analyses, and (5) statistical output files generated from the validation procedures. The raw data files support verification of the benchmark-level and run-level analyses presented in the manuscript, including normalized fitness, relative percentage deviation (RPD), efficiency gain, and the reported inferential statistics. The benchmark reference files provide the structured instance information used in the implementation, including node coordinates, customer demands, vehicle capacities, number of vehicles, and benchmark identifiers. The source code implements the GA framework and the evaluated initialization strategies, while the Python scripts reproduce the statistical validation workflow and generate the reported statistical test results. A README file is included to describe the structure and contents of the uploaded files, software requirements, benchmark configuration procedure, execution workflow, and the steps required to reproduce the reported results. This record is provided to ensure transparency, reproducibility, and independent verification of the study.
DOI: 10.5281/zenodo.20442180
Language: English
Open Access status: green
OpenAlex ID: W7162808228
FWCI:
Cites:
Cited by:
Related to: