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A platform for research: civil engineering, architecture and urbanism
Time- and resource-based robust scheduling algorithms for multi-skilled projects
Abstract The presence of uncertainties often disrupts the implementation of a project plan. This paper investigates robust scheduling methods for multi-skilled projects with stochastic duration. A new indicator for evaluating robustness is designed, which is used to propose a reinforcement learning-based time buffer insertion algorithm and a resource flow optimization process. An integrated time- and resource-based robust scheduling algorithm is also developed, which considers the interaction between time buffers and resource flow. Computational experiments show that the new indicator accurately evaluates the robustness of a plan, and the proposed integrated algorithm significantly outperforms traditional methods. The results demonstrate that systematically optimizing time and resources provides greater assurance that the project will proceed as planned compared to optimizing them separately.
Highlights A MILP model is established for robust multi-skilled project scheduling. Stochastic activity durations are considered in the proposed model. A scheduling algorithm based on reinforcement learning is developed. An indicator for evaluating the robustness is designed in the algorithm. Time- and resource-based approaches are innovatively integrated in the algorithm.
Time- and resource-based robust scheduling algorithms for multi-skilled projects
Abstract The presence of uncertainties often disrupts the implementation of a project plan. This paper investigates robust scheduling methods for multi-skilled projects with stochastic duration. A new indicator for evaluating robustness is designed, which is used to propose a reinforcement learning-based time buffer insertion algorithm and a resource flow optimization process. An integrated time- and resource-based robust scheduling algorithm is also developed, which considers the interaction between time buffers and resource flow. Computational experiments show that the new indicator accurately evaluates the robustness of a plan, and the proposed integrated algorithm significantly outperforms traditional methods. The results demonstrate that systematically optimizing time and resources provides greater assurance that the project will proceed as planned compared to optimizing them separately.
Highlights A MILP model is established for robust multi-skilled project scheduling. Stochastic activity durations are considered in the proposed model. A scheduling algorithm based on reinforcement learning is developed. An indicator for evaluating the robustness is designed in the algorithm. Time- and resource-based approaches are innovatively integrated in the algorithm.
Time- and resource-based robust scheduling algorithms for multi-skilled projects
Zhentao, Hu (author) / Nanfang, Cui (author) / Xuejun, Hu (author) / Edgar Mahaffey, M.A. (author)
2023-05-21
Article (Journal)
Electronic Resource
English
Augmented heuristic algorithm for multi-skilled resource scheduling
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Augmented heuristic algorithm for multi-skilled resource scheduling
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