scholarly journals Robust ranking and selection with optimal computing budget allocation

Automatica ◽  
2017 ◽  
Vol 81 ◽  
pp. 30-36 ◽  
Author(s):  
Siyang Gao ◽  
Hui Xiao ◽  
Enlu Zhou ◽  
Weiwei Chen
Keyword(s):  
Budget Allocation ◽  
Ranking And Selection ◽  
Optimal Computing Budget Allocation

scholarly journals DA-OCBA: Distributed Asynchronous Optimal Computing Budget Allocation Algorithm of Simulation Optimization Using Cloud Computing

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1297 ◽  
Author(s):  
Yukai Wang ◽  
Wenjie Tang ◽  
Yiping Yao ◽  
Feng Zhu
Keyword(s):  
Cloud Computing ◽  
Systems Engineering ◽  
Simulation Optimization ◽  
Budget Allocation ◽  
Ranking And Selection ◽  
Allocation Rule ◽  
Computing Power ◽  
Computing Platform ◽  
Optimal Computing Budget Allocation ◽  
Selection Algorithms

The ranking and selection of simulation optimization is a very powerful tool in systems engineering and operations research. Due to the influence of randomness, the algorithms for ranking and selection need high and uncertain amounts of computing power. Recent advances in cloud computing provide an economical and flexible platform to execute these algorithms. Among all ranking and selection algorithms, the optimal computing budget allocation (OCBA) algorithm is one of the most efficient. However, because of the lack of sufficient samples that can be executed in parallel at each stage, some features of the cloud-computing platform, such as parallelism, scalability, flexibility, and symmetry, cannot be fully utilized. To solve these problems, this paper proposes a distributed asynchronous OCBA (DA-OCBA) algorithm. Under the framework of parallel asynchronous simulation, this algorithm takes advantage of every idle docker container to run better designs in advance that are selected by an asymptotic allocation rule. The experiment demonstrated that the efficiency of simulation optimization for DA-OCBA was clearly higher than that for the traditional OCBA on the cloud platform with symmetric architecture. As the number of containers grew, the speedup of DA-OCBA was linearly increasing for simulation optimization.

A Study on Efficient Computing Budget Allocation for a Two-Stage Problem

2021 ◽  
pp. 2050044
Author(s):  
Tianxiang Wang ◽  
Jie Xu ◽  
Jian-Qiang Hu
Keyword(s):  
Optimization Problem ◽  
Simulation Optimization ◽  
Risk Measure ◽  
Budget Allocation ◽  
Optimal Decision ◽  
Two Stage ◽  
Second Stage ◽  
Allocation Procedure ◽  
Optimal Computing Budget Allocation ◽  
Sequential Allocation

We consider how to allocate simulation budget to estimate the risk measure of a system in a two-stage simulation optimization problem. In this problem, the first stage simulation generates scenarios that serve as inputs to the second stage simulation. For each sampled first stage scenario, the second stage procedure solves a simulation optimization problem by evaluating a number of decisions and selecting the optimal decision for the scenario. It also provides the estimated performance of the system over all sampled first stage scenarios to estimate the system’s reliability or risk measure, which is defined as the probability of the system’s performance exceeding a given threshold under various scenarios. Usually, such a two-stage procedure is very computationally expensive. To address this challenge, we propose a simulation budget allocation procedure to improve the computational efficiency for two-stage simulation optimization. After generating first stage scenarios, a sequential allocation procedure selects the scenario to simulate, followed by an optimal computing budget allocation scheme that determines the decision to simulate in the second stage simulation. Numerical experiments show that the proposed procedure significantly improves the efficiency of the two-stage simulation optimization for estimating system’s reliability.

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