Stochastic Model for Optimal Selection of DDG by Monte Carlo Simulation

2006 ◽  
Author(s):  
N. Vaitheeswaran ◽  
R. Balasubramanian
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Stochastic Model ◽  
Optimal Selection ◽  
Selection Of

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

MONTE CARLO SIMULATION OF THE KINETICS IN THE GROWTH OF SEMICONDUCTOR QUANTUM DOTS

2011 ◽  
Vol 25 (07) ◽  
pp. 465-471 ◽  
Author(s):  
CHANG ZHAO ◽  
M. ZHAO ◽  
Y. WANG ◽  
A. J. LV ◽  
G. M. WU ◽  
...  
Keyword(s):  
Quantum Dots ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Molecular Beam ◽  
Kinetic Monte Carlo ◽  
Semiconductor Quantum Dots ◽  
Dangling Bond ◽  
Good Qualitative Agreement ◽  
Kinetic Effects ◽  
Selection Of

By means of kinetic Monte Carlo simulation, which is based on the random selection of the surface hops of single adatom, we investigate the atoms' kinetics during the growth of the semiconductor quantum dots in a molecular beam epitaxy system, the deposition, diffusion and nucleation are considered as the main relevant processes during the growth of the quantum dots, taking into account the contribution of the dangling bond of the adatoms in the simulation. The dependence of the quantum dot size on the temperature and flux as well as the atomic kinetic effects are discussed in detail. The simulation results are in good qualitative agreement with those of the experiment.

scholarly journals Procedure for Determining the Uncertainties in the Modeling of Surface Roughness in the Turning of NiTi Alloys Using the Monte Carlo Method

Materials ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 4338 ◽  
Author(s):  
Małgorzata Kowalczyk ◽  
Krzysztof Tomczyk
Keyword(s):  
Surface Roughness ◽  
Monte Carlo ◽  
Point Of View ◽  
Machining Process ◽  
Machining Parameters ◽  
Optimal Selection ◽  
Niti Alloys ◽  
The Monte Carlo Method ◽  
Selection Of

The paper presents a procedure for the determination of uncertainties in the modeling of surface roughness in the turning of NiTi alloys. The presented procedure is applicable both to the analysis of the measurement values of the two main roughness factors, as well as to research related to the prediction and optimization of the machining process. Type A and B, total, and expanded uncertainties were considered herein, and the obtained uncertainty values were assessed. A procedure for optimizing machining by applying the Monte Carlo (MC) method is also presented. The solutions presented in this paper are important from the point of view of practical solutions related to the prediction and optimization of the machining process. The considered procedure for determining and assessing uncertainty can be useful for the optimal selection of both machining parameters and measuring tools.

scholarly journals Worker Movement Diagram Based Stochastic Model of Open Paced Conveyors

2018 ◽  
Vol 46 (2) ◽  
pp. 55-62 ◽  
Author(s):  
Tamás Ruppert ◽  
János Abonyi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Human Resources ◽  
Stochastic Model ◽  
Manufacturing Systems ◽  
Production Systems ◽  
Benchmark Problem ◽  
Stochastic Nature ◽  
Moving Patterns ◽  
Open Nature

Abstract Human resources are still utilized in many manufacturing systems, so the development of these processes should also focus on the performance of the operators. The optimization of production systems requires accurate and reliable models. Due to the complexity and uncertainty of the human behavior, the modeling of the operators is a challenging task. Our goal is to develop a worker movement diagram based model that considers the stochastic nature of paced open conveyors. The problem is challenging as the simulator has to handle the open nature of the workstations, which means that the operators can work ahead or try to work off their backlog, and due to the increased flexibility of the moving patterns the possible crossings which could lead to the stopping of the conveyor should also be modeled. The risk of such micro-stoppings is calculated by Monte-Carlo simulation. The applicability of the simulator is demonstrated by a well-documented benchmark problem of a wire-harness production process.

scholarly journals Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

2021 ◽  
Author(s):  
Mohammad Ammar Alzarrad
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Possibility Theory ◽  
Resource Planning ◽  
Fuzzy Arithmetic ◽  
Fuzzy Variables ◽  
Research Outcomes ◽  
The Cost ◽  
Resources Planning ◽  
Selection Of

Resources planning and operations are essential concerns and specialty areas within industrial engineering and project management. Crew configuration plays a significant role in resource planning and operations. Crew configuration inefficiency is one of the most common reasons for the low productivity of manpower. Resources planning contains some inherent uncertainties and risks because it is an estimate of unknown values. Many factors affect resource planning. Some of these factors are fuzzy variables such as expert’s judgment, and some of them are random variables such as direct cost of equipment. The objective of this chapter is to present a method that combines fuzzy logic and Monte Carlo simulation (MCS) for the selection of the best crew configuration to perform a certain task. The model presented in this chapter is a joint propagation method based on both the probability theory of MCS and the possibility theory of fuzzy arithmetic. The research outcomes indicate that the presented model can reduce the duration and cost of a certain task, which will help reduce the cost and duration of the project.

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