Optimal Budget Allocation Rule for Simulation Optimization Using Quadratic Regression in Partitioned Domains

2015 ◽  
Vol 45 (7) ◽  
pp. 1047-1062 ◽  
Author(s):  
Hui Xiao ◽  
Loo Hay Lee ◽  
Chun-Hung Chen
Keyword(s):  
Simulation Optimization ◽  
Budget Allocation ◽  
Allocation Rule ◽  
Quadratic Regression

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.

scholarly journals Efficient Simulation Budget Allocation for Ranking the TopmDesigns

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Hui Xiao ◽  
Loo Hay Lee
Keyword(s):  
Convergence Rate ◽  
Optimal Allocation ◽  
Large Deviation ◽  
Budget Allocation ◽  
Rate Function ◽  
Sequential Algorithm ◽  
Allocation Rule ◽  
Asymptotically Optimal ◽  
Efficient Simulation ◽  
Optimal Computing Budget Allocation

We consider the problem of ranking the topmdesigns out ofkalternatives. Using the optimal computing budget allocation framework, we formulate this problem as that of maximizing the probability of correctly ranking the topmdesigns subject to the constraint of a fixed limited simulation budget. We derive the convergence rate of the false ranking probability based on the large deviation theory. The asymptotically optimal allocation rule is obtained by maximizing this convergence rate function. To implement the simulation budget allocation rule, we suggest a heuristic sequential algorithm. Numerical experiments are conducted to compare the effectiveness of the proposed simulation budget allocation rule. The numerical results indicate that the proposed asymptotically optimal allocation rule performs the best comparing with other allocation rules.

Bootstrap-based Budget Allocation for Nested Simulation

2021 ◽  
Author(s):  
Kun Zhang ◽  
Guangwu Liu ◽  
Shiyu Wang
Keyword(s):  
Confidence Intervals ◽  
Financial Risk ◽  
Budget Allocation ◽  
Risk Measurement ◽  
Allocation Rule ◽  
Sample Sizes ◽  
Bootstrap Sampling ◽  
Asymptotically Optimal ◽  
Optimal Sample ◽  
Asymptotic Validity

Nested simulation (also referred to as two-level simulation) finds a variety of applications such as financial risk measurement, and a central issue of nested simulation is how to allocate a finite amount of simulation budget to achieve the highest accuracy. In “Bootstrap-based Budget Allocation for Nested Simulation”, Zhang, Liu, and Wang propose a bootstrap-based rule for simulation budget allocation for nested simulation. By utilizing the asymptotically optimal inner- and outer-level sample sizes that are typically unknown, the proposed method employs bootstrap sampling on a small amount of initial samples to estimate the unknown optimal sample sizes, thus providing a reasonably good allocation rule for the main simulation. An allocation rule to ensure the asymptotic validity of confidence intervals is also given.

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