Decomposition methods for resourced constrained project management problems with uncertainty
- Publication Type:
- Thesis
- Issue Date:
- 2024
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The resource-constrained project scheduling problem (RCPSP) has gained significant attention due to its NP-hard nature and diverse applications in mining, manufacturing, and supply chains. This thesis focuses on RCPSP and its two extensions: the Multimode RCPSP (MRCPSP) and the Stochastic RCPSP (SRCPSP). Novel hybrid metaheuristic, matheuristic, and approximate dynamic programming approaches are developed to address these challenges.
For RCPSP, a novel hybrid metaheuristic based on the Artificial Bee Colony (ABC) algorithm is proposed. This method enhances the ABC's global search ability by integrating a powerful local search, effectively tackling RCPSP's complexities.
For MRCPSP, a new matheuristic combines iterative problem relaxation and a mathematical programming model generalising the multidimensional knapsack problem. This approach outperforms state-of-the-art methods on benchmark instances.
To solve SRCPSP, an approximate dynamic programming approach utilises a deterministic solution to reduce the computational burden of stochastic task durations. A novel matheuristic generates initial solutions, enabling efficient evaluation.
The proposed methodologies demonstrate superior performance across benchmark tests, offering competitive solutions in reduced computational times, advancing the state-of-the-art in solving RCPSP and its extensions.
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