.png){kind=icon}
返回
Simulation-Based Robust and Adaptive Optimization Method for Heteroscedastic Transportation Problems
.png){kind=icon}
PRE
AI
DOI：10.1287/trsc.2023.0485.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Simulation -based optimization is an effective solution to complex transportation problems relying on stochastic simulations. However, existing studies generally perform a fixed number of evaluations for each decision vector across the design space, overlooking simulation heteroscedasticity and its effects on solution efficiency and robustness. In this paper, we treat the number of evaluations as an adaptive variable depending upon the simulation heteroscedasticity and the potential optimality of each decision vector. A statistical method, which automatically determines the variable number of evaluations, is presented for a range of derivative -free optimization methods. By fusing Bayesian inference with the probability of correct selection, it permits adaptive allocation of budgeted computational resources to achieve improved solution efficiency and robustness. The method is integrated with the deterministic global optimizer DIviding RECTangles (DIRECT) to yield NoisyDIRECT as a continuous simulation -based robust optimization method (which is open sourced). The key properties of the method are proved and discussed. Numerical experiments on difficult test functions are first conducted to verify the improvement of NoisyDIRECT compared with DIRECT and Bayesian optimization. Given the same computational budget, NoisyDIRECT can better locate the global optimum than the other two alternatives. Applications to representative simulation -based transportation problems, including an M/ M/1 queueing problem and a parking pricing problem, are then presented. The results demonstrate the ability of NoisyDIRECT to pinpoint the optimal solution via adaptive computational resources allocation, achieving the desired level of robustness.
Keyword:
simulation-based optimization
robust design
computational resources allocation
Bayesian inference
heteroscedasticity
期刊
IF:
4.8
论文数:
1.9K
被引数:
8.4K
机构
引用论文
.png){kind=icon}
分享收藏Time-of-day pricing for toll roads under traffic demand uncertainties: A distributionally robust simulation-based optimization method交通需求不确定性下收费公路的日时定价: 基于分布鲁棒仿真的优化方法
PREAI
分享收藏
A Computationally Efficient Simulation-Based Optimization Algorithm for Large-Scale Urban Transportation Problems大规模城市交通问题的计算效率高的基于仿真的优化算法
PREAI
分享收藏分享收藏
查看更多内容