A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916880990601216 |
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| author | Shavazipour, Babooshka Stewart, Theodor J. |
| author_facet | Shavazipour, Babooshka Stewart, Theodor J. |
| contents | Many real-world decision-making problems involve multiple decision-making stages and various objectives. Besides, most of the decisions need to be made before having complete knowledge about all aspects of the problem leaves some sort of uncertainty. Deep uncertainty happens when the degree of uncertainty is so high that the probability distributions are not confidently knowable. In this situation, using wrong probability distributions leads to failure. Scenarios, instead, should be used to evaluate the consequences of any decisions in different plausible futures and find a robust solution. In this study, we proposed a novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty. Two approaches, named multi-stage multi-scenario multi-objective and two-stage moving horizon, have been proposed and compared. Finally, the proposed approaches are applied in a case study of sequential portfolio selection under deep uncertainty, and the robustness of their solutions is discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_11745 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty Shavazipour, Babooshka Stewart, Theodor J. Optimization and Control Many real-world decision-making problems involve multiple decision-making stages and various objectives. Besides, most of the decisions need to be made before having complete knowledge about all aspects of the problem leaves some sort of uncertainty. Deep uncertainty happens when the degree of uncertainty is so high that the probability distributions are not confidently knowable. In this situation, using wrong probability distributions leads to failure. Scenarios, instead, should be used to evaluate the consequences of any decisions in different plausible futures and find a robust solution. In this study, we proposed a novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty. Two approaches, named multi-stage multi-scenario multi-objective and two-stage moving horizon, have been proposed and compared. Finally, the proposed approaches are applied in a case study of sequential portfolio selection under deep uncertainty, and the robustness of their solutions is discussed. |
| title | A novel multi-stage multi-scenario multi-objective optimisation framework for adaptive robust decision-making under deep uncertainty |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2312.11745 |