Insights into Weighted Sum Sampling Approaches for Multi-Criteria Decision Making Problems
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866908324099784704 |
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| author | Williams, Aled Cai, Yilun |
| author_facet | Williams, Aled Cai, Yilun |
| contents | In this paper we explore several approaches for sampling weight vectors in the context of weighted sum scalarisation approaches for solving multi-criteria decision making (MCDM) problems. This established method converts a multi-objective problem into a (single) scalar optimisation problem. It does so by assigning weights to each objective. We outline various methods to select these weights, with a focus on ensuring computational efficiency and avoiding redundancy. The challenges and computational complexity of these approaches are explored and numerical examples are provided. The theoretical results demonstrate the trade-offs between systematic and randomised weight generation techniques, highlighting their performance for different problem settings. These sampling approaches will be tested and compared computationally in an upcoming paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03931 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Insights into Weighted Sum Sampling Approaches for Multi-Criteria Decision Making Problems Williams, Aled Cai, Yilun Optimization and Control 90C29, 90B50, 65K10 In this paper we explore several approaches for sampling weight vectors in the context of weighted sum scalarisation approaches for solving multi-criteria decision making (MCDM) problems. This established method converts a multi-objective problem into a (single) scalar optimisation problem. It does so by assigning weights to each objective. We outline various methods to select these weights, with a focus on ensuring computational efficiency and avoiding redundancy. The challenges and computational complexity of these approaches are explored and numerical examples are provided. The theoretical results demonstrate the trade-offs between systematic and randomised weight generation techniques, highlighting their performance for different problem settings. These sampling approaches will be tested and compared computationally in an upcoming paper. |
| title | Insights into Weighted Sum Sampling Approaches for Multi-Criteria Decision Making Problems |
| topic | Optimization and Control 90C29, 90B50, 65K10 |
| url | https://arxiv.org/abs/2410.03931 |