An efficient two-stage heuristic stratification and neyman-allocation procedure

A. Drexl

doi:10.1007/bf01720240

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

Asia Pacific Journal of Operational Research ◽

10.1142/s021759592050044x ◽

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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AN INTEGRATED TWO-STAGE METHODOLOGY FOR OPTIMISING THE ACCURACY OF PERFORMANCE CLASSIFICATION MODELS

Technological and Economic Development of Economy ◽

10.3846/20294913.2016.1213196 ◽

2017 ◽

Vol 23 (1) ◽

pp. 111-139 ◽

Cited By ~ 5

Author(s):

Adrian COSTEA ◽

Massimiliano FERRARA ◽

Florentin ŞERBAN

Keyword(s):

Financial Performance ◽

Poor Performance ◽

Classification Model ◽

Second Phase ◽

Classification Models ◽

Feed Forward Neural Network ◽

Two Stage ◽

Allocation Procedure ◽

Class Variable ◽

A Performance

In this paper we propose a two-stage methodology to classify the non-banking financial institutions (NFIs) based on their financial performance. The first stage of the methodology consists of grouping the companies in similar financial performance classes (e.g.: “good”, “average”, “poor” performance classes). We optimise the allocation of the observations within the performance clusters by applying an enhanced version of an observation re-allocation procedure proposed in our previous work. Next, based on the result of the grouping phase, we construct a performance class variable by attaching a performance label to each data row. Then, in the second phase of our methodology, we propose a feed-forward neural-network classification model that maps the input space to the newly-constructed performance class variable. This model allows us to forecast the performance of new companies as data become available.

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Efficiency of Neyman Allocation Procedure over other Allocation Procedures in Stratified Random Sampling

American Journal of Theoretical and Applied Statistics ◽

10.11648/j.ajtas.20130205.12 ◽

2013 ◽

Vol 2 (5) ◽

pp. 122 ◽

Cited By ~ 5

Author(s):

Olayiwola Olaniyi Mathew

Keyword(s):

Random Sampling ◽

Stratified Random Sampling ◽

Allocation Procedure ◽

Neyman Allocation

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Two-stage self-seeding method for preparation of single crystals of poly(phenylene sulfide)

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100153816 ◽

1989 ◽

Vol 47 ◽

pp. 368-369

Author(s):

Sengshiu Chung ◽

Peggy Cebe

Keyword(s):

Single Crystals ◽

High Performance ◽

Dilute Solution ◽

Two Stage ◽

Annealing Behavior ◽

High Performance Polymers ◽

Seeding Method ◽

Phenylene Sulfide

We are studying the crystallization and annealing behavior of high performance polymers, like poly(p-pheny1ene sulfide) PPS, and poly-(etheretherketone), PEEK. Our purpose is to determine whether PPS, which is similar in many ways to PEEK, undergoes reorganization during annealing. In an effort to address the issue of reorganization, we are studying solution grown single crystals of PPS as model materials.Observation of solution grown PPS crystals has been reported. Even from dilute solution, embrionic spherulites and aggregates were formed. We observe that these morphologies result when solutions containing uncrystallized polymer are cooled. To obtain samples of uniform single crystals, we have used two-stage self seeding and solution replacement techniques.

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