Monte Carlo simulation of classical swine fever epidemics and control

Randomized pretest, posttest, follow-up (RPPF) designs are often used for evaluating the effectiveness of an intervention. These designs typically address two primary research questions: (1) Do the treatment and control groups differ in the amount of change from pretest to posttest? and (2) Do the treatment and control groups differ in the amount of change from posttest to follow-up? This study presents a model for answering these questions and compares it to recently proposed models for analyzing RPPF designs due to Mun, von Eye, and White (2009) using Monte Carlo simulation. The proposed model provides increased power over previous models for evaluating group differences in RPPF designs.

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Monte Carlo Simulation Experiments for Engineering Optimisation

Studies in Engineering and Technology ◽

10.11114/set.v2i1.901 ◽

2015 ◽

Vol 2 (1) ◽

pp. 97

Author(s):

Robert Anderson ◽

Zhou Wei ◽

Ian Cox ◽

Malcolm Moore ◽

Florence Kussener

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Tolerance Analysis ◽

Simulation Experiments ◽

Practical Applications ◽

Study Approach ◽

Good Set ◽

Robust Process ◽

And Control

Design of Experiments (DoE) is widely used in design, manufacturing and quality management. The resulting data is usually analysed with multiple linear regression to generate polynomial equations that describe the relationship between process inputs and outputs. These equations enable us to understand how input values affect the predicted value of one or more outputs and find good set points for the inputs. However, to develop robust manufacturing processes, we also need to understand how variation in these inputs appears as variation in the output. This understanding allows us to define set points and control tolerances for the inputs that will keep the outputs within their required specification windows. Tolerance analysis provides a powerful way of finding input settings and ranges that minimise output variation to produce a process that is robust. In many practical applications, tolerance analysis exploits Monte Carlo simulation of the polynomial model generated from DoE’s. This paper briefly describes tolerance analysis and then shows how Monte Carlo simulation experiments using space-filling designs can be used to find the input settings that result in a robust process. Using this approach, engineers can quickly and easily identify the key inputs responsible for transferring undesired variation to their process outputs and identify the set points and ranges that make their process as robust as possible. If the process is not sufficiently robust, they can rationally investigate different strategies to improve it. A case study approach is used to aid explanation and understanding.

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Experimental Error Measurement in Monte Carlo Simulation

Handbook of Research on Discrete Event Simulation Environments ◽

10.4018/978-1-60566-774-4.ch006 ◽

2010 ◽

pp. 92-142 ◽

Cited By ~ 2

Author(s):

Lucia Cassettari ◽

Roberto Mosca ◽

Roberto Revetria

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Experimental Error ◽

System Simulation ◽

Simulation Models ◽

Error Measurement ◽

Mean Square ◽

Optimal Functioning ◽

Set Up ◽

And Control

This chapter describes the set up step series, developed by the Genoa Research Group on Production System Simulation at the beginning of the ’80s, as a sequence, through which it is possible at first statistically validate the simulator, then estimate the variables which effectively affect the different target functions, then obtain, through the regression meta-models, the relations linking the independent variables to the dependent ones (target functions) and, finally, proceed to the detection of the optimal functioning conditions. The authors pay great attention to the treatment, the evaluation and control of the Experimental Error, under the form of Mean Square Pure Error (MSPE), a measurement which is always culpably neglected in the traditional experimentation on the simulation models but, that potentially can consistently invalidate with its magnitude the value of the results obtained from the model.

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Application of Artificial Neural Networks Based Monte Carlo Simulation in the Expert System Design and Control of Crude Oil Distillation Column of a Nigerian Refinery

Advances in Chemical Engineering and Science ◽

10.4236/aces.2014.42030 ◽

2014 ◽

Vol 04 (02) ◽

pp. 266-283 ◽

Cited By ~ 3

Author(s):

Lekan T. Popoola ◽

Alfred A. Susu

Keyword(s):

Neural Networks ◽

Monte Carlo Simulation ◽

Monte Carlo ◽

Artificial Neural Networks ◽

Expert System ◽

Crude Oil ◽

System Design ◽

Distillation Column ◽

Crude Oil Distillation ◽

And Control

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A Project Monitoring and Control System Using EVM and Monte Carlo Simulation

Lecture Notes in Management and Industrial Engineering - Project Management and Engineering Research, 2014 ◽

10.1007/978-3-319-26459-2_3 ◽

2015 ◽

pp. 31-40

Author(s):

Fernando Acebes ◽

Javier Pajares ◽

José Manuel Galán ◽

Adolfo López-Paredes

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Control System ◽

Monitoring And Control ◽

Monitoring And Control System ◽

Project Monitoring ◽

And Control

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Requirements Derivation for RoboCup Small Size League Robot

10.48011/asba.v2i1.1544 ◽

2020 ◽

Author(s):

Gabriel B. Farias ◽

Marcos R. O. A. Máximo ◽

Rubens J. M. Afonso

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Mobile Robot ◽

Robot Soccer ◽

Control Laws ◽

And Control ◽

Parameter Values ◽

Parameters Of Interest

This work develops a method for deriving requirements for the goalkeeper of the robot soccer competition RoboCup Small Size League (SSL) using Monte Carlo simulation. Initially, an overview of the SSL competition is presented and related works are shown. Then, the parameters of interest are selected and the developed method is discussed. Afterwards, different models and control laws are designed to simulate the goal defense performance for different parameter values. Finally, the data generated is analyzed and a set of requirements for the mobile robot is selected. Lastly, the method utility is evaluated and possible extensions of this work are proposed.

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Construction Engineering Bidding Risk Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.671-674.2990 ◽

2013 ◽

Vol 671-674 ◽

pp. 2990-2994

Author(s):

Fan Jing Yu ◽

Jun Jie Li

Keyword(s):

Monte Carlo Simulation ◽

Risk Management ◽

Monte Carlo ◽

Construction Projects ◽

Simulation Method ◽

Bidding Strategy ◽

Monte Carlo Simulation Method ◽

Tender Offer ◽

Construction Engineering ◽

And Control

In the Bill of Quantities mode, the bidder must take risks of his tender offer. This paper researched on the uncertainty of construction projects costs, proposed Monte Carlo simulation method and procedure to simulate the construction projects costs on the basis of the construction projects costs conform to the characteristics of normal distribution, and also put forward a method to calculate risks of the tender offer in accordance with the established bidding strategy, which are helpful to the risk management and control of project contracting enterprises.

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Comparison of different control strategies for classical swine fever using emergency vaccination and rapid PCR testing by using a Monte-Carlo simulation model

Archives Animal Breeding ◽

10.7482/0003-9438-56-100 ◽

2013 ◽

Vol 56 (1) ◽

pp. 988-1004 ◽

Cited By ~ 1

Author(s):

J. Brosig ◽

I. Traulsen ◽

S. Blome ◽

K. Depner ◽

J. Krieter

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Simulation Model ◽

Control Strategies ◽

Control Measure ◽

Classical Swine Fever ◽

Current Control ◽

Control Measures ◽

Alternative Control ◽

Monte Carlo Simulation Model

Abstract. Whenever an outbreak of classical swine fever has occurred in the European Union (EU), the basic control measures have usually been supplemented by preventive culling. This strategy has led to a great number of culled pigs and is discussed by general public and politics from both ethical and economic points of view. Emergency vaccination has been deemed to be an alternative control measure for some time now. PCR testing also provides a possible future strategy, since this method would allow a rapid and reliable testing of pigs in the vicinity of an outbreak farm. In this study, a spatial and temporal Monte-Carlo simulation model was used to compare alternative control strategies based upon these two measures (»Emergency Vaccination«, »Test To Slaughter«, »Test To Control« and »Vaccination in conjunction with Rapid Testing«) with the current control strategy. Two regions for investigation with different farm densities were used in the model. In a region with a low farm density, the basic EU control measures seemed to be sufficient to control an epidemic. In a region with a high farm density, additional measures would be necessary. »Emergency Vaccination« in a 3 km application zone and »Traditional Control« reached the same level of infected farms. Both »Test To Slaughter« and »Test To Control« combined with preventive culling led to a lower number of infected farms compared to the sole preventive culling strategy. The alternative control measures can reduce the number of culled farms significantly compared to »Traditional Control«.

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A cluster-autonomous partitioning algorithm in electrical power grid using Monte Carlo simulation

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962317500532 ◽

2018 ◽

Vol 09 (01) ◽

pp. 1750053 ◽

Cited By ~ 1

Author(s):

Chengchao Lu ◽

Zhongjie Wang

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Power Grid ◽

Electrical Power ◽

Monitoring And Control ◽

Large Power ◽

Grid Partitioning ◽

Binary Coding ◽

And Control ◽

Bus System

Power grid partitioning decomposes a large power grid into several clusters. Most of the existing partitioning methods suffer from a limitation that the buses within a cluster are severely topologically disconnected after partitioning in some cases. As a result, a cluster will inevitably be assigned to two or more power grid corporations. This assignment obstructs inner-cluster monitoring and control applications of the transmission system. To overcome the limitation, this paper proposes a multi-index power grid partitioning approach using Monte Carlo simulation guaranteeing cluster connectivity to ensure the cluster autonomy. A line-based binary coding technique is developed to ensure the cluster connectivity. Three partitioning indices are considered: the coherency, the cluster connectivity, and the number of clusters. Finally, the proposed partitioning method is applied to IEEE 9-bus system, IEEE 39-bus system and IEEE 145-bus system and compared with Fuzzy C-medoid (FCMdd) algorithm.

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