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Processed Construction Task Data for Multi-Objective Optimization of Construction Scheduling Based on Grey Wolf Optimization Algorithm [dataset] Open Access

This study proposes a data-supported multi-objective optimization model for construction scheduling, integrating structured task-resource datasets to improve decision-making. Using Grey Wolf Optimization (GWO), the model aims to minimize project duration while maximizing resource utilization, addressing challenges in scheduling feasibility. Comparative experiments with Particle Swarm Optimization (PSO) and Sparrow Search Algorithm (SSA) demonstrate that GWO achieves better optimization stability and constraint handling, reducing premature convergence and improving solution feasibility. Although the current model relies on static datasets, future research could enhance adaptability by incorporating real-time project data and machine learning-driven scheduling predictions.

Descriptions

Resource type

Dataset

Contributors

Creator: Sun, Yingpei ¹
Wang, Qing ¹
Toll, David ¹
Szyniszewski, Stefan ¹

¹ Durham University, UK

Funder

Research methods

This dataset was compiled from multiple academic sources on construction scheduling. The raw data was manually extracted, cleaned, and structured into an Excel format. The processing steps included standardizing time units, defining task dependencies, and structuring productivity data. The dataset is suitable for use in construction scheduling optimization and multi-objective decision analysis.

Other description

This dataset is structured to support research in construction scheduling, task optimization, and resource allocation. It includes task dependencies, material requirements, and productivity rates.

Keyword

Construction scheduling
Prefabrication
Material logistics
Task dependencies
Productivity analysis
Optimization

Subject

Construction projects
Production scheduling

Location

Language

English

Cited in

Identifier

doi:10.15128/r1nz805z75v

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

2025-02-21

File Details

Depositor

Y. Sun

Date Uploaded

21 February 2025, 02:02:46

Date Modified

21 February 2025, 10:02:51

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Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 25562

Last modified: 2025:02:21 02:24:54+00:00

Filename: Construction_Task_ProcessedData.zip

Original checksum: 4775dc612bd41ceac0f3c35ac73ed7e6