Adaptive Partitioning Strategy for High-Dimensional Discrete Simulation-based Optimization Problems
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2021
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| _version_ | 1866915044790370304 |
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| author | Lu, Jing Zhou, Tianli Osorio, Carolina |
| author_facet | Lu, Jing Zhou, Tianli Osorio, Carolina |
| contents | In this paper, we introduce a technique to enhance the computational efficiency of solution algorithms for high-dimensional discrete simulation-based optimization problems. The technique is based on innovative adaptive partitioning strategies that partition the feasible region using solutions that has already been simulated as well as prior knowledge of the problem of interesting. We integrate the proposed strategies with the Empirical Stochastic Branch-and-Bound framework proposed by Xu and Nelson (2013). This combination leads to a general-purpose discrete simulation-based optimization algorithm that is both globally convergent and has good small sample (finite-time) performance. The proposed general-purpose discrete simulation-based optimization algorithm is validated on a synthetic discrete simulation-based optimization problem and is then used to address a real-world car-sharing fleet assignment problem. Experiment results show that the proposed strategy can increase the algorithm efficiency significantly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_14119 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Adaptive Partitioning Strategy for High-Dimensional Discrete Simulation-based Optimization Problems Lu, Jing Zhou, Tianli Osorio, Carolina Optimization and Control Computational Engineering, Finance, and Science Systems and Control In this paper, we introduce a technique to enhance the computational efficiency of solution algorithms for high-dimensional discrete simulation-based optimization problems. The technique is based on innovative adaptive partitioning strategies that partition the feasible region using solutions that has already been simulated as well as prior knowledge of the problem of interesting. We integrate the proposed strategies with the Empirical Stochastic Branch-and-Bound framework proposed by Xu and Nelson (2013). This combination leads to a general-purpose discrete simulation-based optimization algorithm that is both globally convergent and has good small sample (finite-time) performance. The proposed general-purpose discrete simulation-based optimization algorithm is validated on a synthetic discrete simulation-based optimization problem and is then used to address a real-world car-sharing fleet assignment problem. Experiment results show that the proposed strategy can increase the algorithm efficiency significantly. |
| title | Adaptive Partitioning Strategy for High-Dimensional Discrete Simulation-based Optimization Problems |
| topic | Optimization and Control Computational Engineering, Finance, and Science Systems and Control |
| url | https://arxiv.org/abs/2104.14119 |