An improved multiobjective genetic algorithm for robustness-cost trade-off optimization in resource-constrained proactive project scheduling

Proactive project scheduling research investigates how to allocate buffer times to project activities effectively to enhance the robustness of project schedules. However, when incorporating buffer times into schedules, ensuring that activities can be executed within these buffers requires the alloca...

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Veröffentlicht in:Expert systems with applications Jg. 299; S. 130309
Hauptverfasser: Li, Xue, Zheng, Shuang, He, Zhengwen, Zheng, Lanlan, Li, Yuanbo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2026
Schlagworte:
Genetic algorithm
Optimization model
Proactive project scheduling
Robustness cost
Trade-off
Robustness cost
Genetic algorithm
Trade-off
Proactive project scheduling
Optimization model
ISSN:0957-4174
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Zusammenfassung:Proactive project scheduling research investigates how to allocate buffer times to project activities effectively to enhance the robustness of project schedules. However, when incorporating buffer times into schedules, ensuring that activities can be executed within these buffers requires the allocation of additional resources. This leads to robustness costs, which can affect project profitability and limit improvements in schedule robustness. To address this challenge, this paper explores the optimization of the trade-off between robustness and cost during schedule development. First, drawing on real-world project scenarios, the objective of minimizing robustness costs is established, combined with the goal of maximizing robustness. This forms the basis for a robustness-cost trade-off optimization model for resource-constrained proactive project scheduling. The paper further analyzes the characteristics and properties of the proposed model. Next, an enhanced multiobjective genetic algorithm is designed to solve the robustness-cost trade-off optimization problem, tailored to the structure and characteristics of the model. Finally, computational experiments are conducted to compare the performance of the proposed algorithm with various established multiobjective algorithms, thereby demonstrating its efficiency and effectiveness. Key factors influencing solution quality are analyzed. These results reveal patterns in robustness-cost trade-offs and offer valuable managerial insights.
ISSN:0957-4174
DOI:10.1016/j.eswa.2025.130309