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<p>Efficient scheduling of complex engineering projects under resource constraints remains a significant challenge, particularly when tasks exhibit strong interdependencies and iterative workflows. This study addresses a 45-task Resource-Constrained Project Scheduling Problem (RCPSP) using a Dependency Structure Matrix (DSM) to model task precedence and resource interactions. Harris Hawks Optimization (HHO), a nature-inspired metaheuristic, is applied to minimize the project makespan while respecting resource capacities and iterative dependencies. Accordingly, the study is explicitly framed as a single-objective makespan-minimization RCPSP; cost, quality, and resource-leveling indicators are treated as managerial extensions rather than optimized criteria in the present model. The performance of HHO is benchmarked against five well-established algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Ant Colony Optimization (ACO). Computational experiments over 30 independent runs demonstrate that HHO achieves a superior minimum makespan of 171.5 days with a low standard deviation of 1.10, outperforming GA (181.0 days), PSO (179.4 days), GWO (176.8 days), WOA (177.6 days), and ACO (183.2 days). Statistical analysis via Wilcoxon signed-rank and Friedman tests confirms the significance of these improvements. The results highlight HHO’s robust convergence, efficient resource utilization, and ability to handle complex DSM-based project structures. Beyond these specific metrics, this research offers a reliable decision-support framework for project managers, demonstrating that integrating advanced metaheuristics with structured dependency modeling can significantly enhance operational excellence and strategic planning in resource-limited industrial environments.</p>

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Makespan minimization for resource-constrained project scheduling via Harris Hawks optimization

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • Larbi Bendada,
  • Mourad Brioua,
  • Selman Djeffal

摘要

Efficient scheduling of complex engineering projects under resource constraints remains a significant challenge, particularly when tasks exhibit strong interdependencies and iterative workflows. This study addresses a 45-task Resource-Constrained Project Scheduling Problem (RCPSP) using a Dependency Structure Matrix (DSM) to model task precedence and resource interactions. Harris Hawks Optimization (HHO), a nature-inspired metaheuristic, is applied to minimize the project makespan while respecting resource capacities and iterative dependencies. Accordingly, the study is explicitly framed as a single-objective makespan-minimization RCPSP; cost, quality, and resource-leveling indicators are treated as managerial extensions rather than optimized criteria in the present model. The performance of HHO is benchmarked against five well-established algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), and Ant Colony Optimization (ACO). Computational experiments over 30 independent runs demonstrate that HHO achieves a superior minimum makespan of 171.5 days with a low standard deviation of 1.10, outperforming GA (181.0 days), PSO (179.4 days), GWO (176.8 days), WOA (177.6 days), and ACO (183.2 days). Statistical analysis via Wilcoxon signed-rank and Friedman tests confirms the significance of these improvements. The results highlight HHO’s robust convergence, efficient resource utilization, and ability to handle complex DSM-based project structures. Beyond these specific metrics, this research offers a reliable decision-support framework for project managers, demonstrating that integrating advanced metaheuristics with structured dependency modeling can significantly enhance operational excellence and strategic planning in resource-limited industrial environments.