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Related Experiment Video

Updated: May 10, 2025

Operation of the Collaborative Composite Manufacturing CCM System

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

Multi skill project scheduling optimization based on quality transmission and rework network reconstruction.

Junlong Peng¹, Zhuo Su², Xiao Liu³

¹Changsha University of Science and Technology, Changsha, China.

Scientific Reports

|April 19, 2025

Summary

This study introduces a novel project scheduling model to reduce rework by considering personnel skills and dynamic reconstruction. An improved optimization algorithm enhances solution accuracy and efficiency for complex projects.

Keywords:

Cascading effects and their implications Enhanced Gazelle Optimization Algorithm (GOAIP)Multi-Skill Resource-Constrained Project Scheduling Problem Quality transmission mechanism Rework subnet reconstruction

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Area of Science:

Project Management
Operations Research
Artificial Intelligence

Background:

Quality deficiencies significantly increase project rework.
Personnel skill levels are crucial for determining activity quality.
Existing scheduling models often do not adequately address rework dynamics.

Purpose of the Study:

To develop an optimized scheduling model for the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP).
To integrate quality transmission and dynamic rework subnet reconstruction into project scheduling.
To mitigate rework risks and optimize project duration.

Main Methods:

A novel scheduling model was formulated within the MSRCPSP framework.
Quality transmission mechanisms and dynamic rework subnet reconstruction were incorporated.
An Improved Gazelle Optimization Algorithm (GOAIP) with dynamic operators, shuffle crossover, and Gaussian mutation was developed.

Main Results:

The proposed model and GOAIP demonstrated superior performance compared to mainstream optimization techniques.
Higher solution accuracy and improved convergence efficiency were observed.
The approach showed robust applicability and practical significance across various case scales.

Conclusions:

The integrated scheduling model effectively addresses rework risks in MSRCPSP.
The GOAIP provides an efficient method for solving complex scheduling problems with quality considerations.
This research offers a valuable tool for optimizing project duration and quality.