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Comparison stochastic optimisation approaches for the multi-mode resource-constrained multi-project scheduling problem
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DOI：10.1080/23302674.2025.2545517.png)
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Abstract
En 中文
This study addresses the Multi-Mode Resource-Constrained Multi-Project Scheduling Problem (MRCMPSP), which encompasses complex precedence relationships, limited resource capacities, and multiple execution modes across several concurrent projects. A multi-objective optimisation framework was developed to determine the optimal start and finish times for all activities, taking into account resource constraints. The primary objectives include minimising total project duration, reducing overall cost, and maximising project quality. To tackle this challenge, the Multi-objective Giant Pacific Octopus Optimiser (MOGPOO) was employed. The model was evaluated using a comprehensive dataset comprising nine representative construction projects under typical MRCMPSP conditions. Its performance was benchmarked against two other state-of-the-art multi-objective metaheuristics. Experimental results demonstrate that MOGPOO consistently outperforms the competing methods across most evaluation metrics. Furthermore, its advantages become more pronounced as problem complexity increases, confirming its robustness and scalability. These findings contribute valuable insights for researchers and practitioners aiming to optimise multi-project scheduling in resource-constrained environments.
Keywords:
Artificial intelligence
optimization
multi-project
giant Pacific octopus optimizer
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