A dynamic learning-based genetic algorithm for scheduling resource-constrained projects with alternative subgraphs
Nekoueian, Rojin Servranckx, Tom
Nekoueian, Rojin
Servranckx, Tom
Publication Type
Journal article with impact factor
Editor
Supervisor
Publication Year
2025
Journal
Applied Soft Computing
Book
Publication Volume
180
Publication Issue
August
Publication Begin page
113316
Publication End page
Publication Number of pages
Collections
Abstract
Genetic algorithms (GAs) are population-based algorithms widely applied for solving complex scheduling problems and such the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS) in which alternatives for work packages should be selected prior to project scheduling. The objective of this research is twofold. First, we develop a dynamic GA based on a new hybridisation of initialisation procedures, local searches based on learning approaches and restart schemes for scheduling problems in general. Second, we improve existing benchmark solutions for a large artificial dataset for the RCPSP-AS in particular. Our dynamic GA leverages existing constructive heuristics and priority rules to create a pool of high-quality initial solutions. Subsequently, these solutions are further improved by means of learning approaches that are designed as weight- or population-based local searches. In order to avoid getting stuck in a local optimum, various restart schemes are implemented. Based on our results, gradual learning and learning based on the population outperform other approaches for high-complex problem instances. Since metaheuristics — such as GAs — are mainly beneficial in complex problem settings, we are convinced that these research findings can inspire researcher when solving similar or other scheduling problems.
Research Projects
Organizational Units
Journal Issue
Keywords
Project scheduling, Resource-constrained scheduling, Alternative subgraph, Learning, Genetic algorithm