Monte Carlo Simulation of Stochastic Integrals when the Cost of Function Evaluation Is Dimension Dependent

2009 ◽  
pp. 545-560 ◽  
Author(s):  
Ben Niu ◽  
Fred J. Hickernell
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Function Evaluation ◽  
Stochastic Integrals ◽  
The Cost

scholarly journals Uncertainty Cost Functions in Climate-Dependent Controllable Loads in Commercial Environments

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2885
Author(s):  
Daniel Losada ◽  
Ameena Al-Sumaiti ◽  
Sergio Rivera
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Simulation Method ◽  
Electricity Sector ◽  
Cost Functions ◽  
Density Estimator ◽  
Monte Carlo Simulation Method ◽  
Commercial Building ◽  
Complementary Method ◽  
The Cost

This article presents the development, simulation and validation of the uncertainty cost functions for a commercial building with climate-dependent controllable loads, located in Florida, USA. For its development, statistical data on the energy consumption of the building in 2016 were used, along with the deployment of kernel density estimator to characterize its probabilistic behavior. For validation of the uncertainty cost functions, the Monte-Carlo simulation method was used to make comparisons between the analytical results and the results obtained by the method. The cost functions found differential errors of less than 1%, compared to the Monte-Carlo simulation method. With this, there is an analytical approach to the uncertainty costs of the building that can be used in the development of optimal energy dispatches, as well as a complementary method for the probabilistic characterization of the stochastic behavior of agents in the electricity sector.

On Using Monte Carlo Simulations for Project Risk Management

2016 ◽  
pp. 150-173
Author(s):  
Cristiana Tudor ◽  
Maria Tudor
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Decision Makers ◽  
Project Schedule ◽  
Project Risk ◽  
Monte Carlo Simulation Technique ◽  
Basic Probability ◽  
The Cost

This chapter covers the essentials of using the Monte Carlo Simulation technique (MSC) for project schedule and cost risk analysis. It offers a description of the steps involved in performing a Monte Carlo simulation and provides the basic probability and statistical concepts that MSC is based on. Further, a simple practical spreadsheet example goes through the steps presented before to show how MCS can be used in practice to assess the cost and duration risk of a project and ultimately to enable decision makers to improve the quality of their judgments.

scholarly journals A Case Study for Business Integration as a Service

2014 ◽  
pp. 229-254 ◽  
Author(s):  
Victor Chang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis ◽  
Return On Investment ◽  
Cost Savings ◽  
Computational Results ◽  
Cloud Adoption ◽  
The Cost ◽  
The University

This chapter presents Business Integration as a Service (BIaaS) to allow two services to work together in the Cloud to achieve a streamline process. The authors illustrate this integration using two services, Return on Investment (ROI) Measurement as a Service (RMaaS) and Risk Analysis as a Service (RAaaS), in the case study at the University of Southampton. The case study demonstrates the cost-savings and the risk analysis achieved, so two services can work as a single service. Advanced techniques are used to demonstrate statistical services and 3D Visualisation services under the remit of RMaaS and Monte Carlo Simulation as a Service behind the design of RAaaS. Computational results are presented with their implications discussed. Different types of risks associated with Cloud adoption can be calculated easily, rapidly, and accurately with the use of BIaaS. This case study confirms the benefits of BIaaS adoption, including cost reduction and improvements in efficiency and risk analysis. Implementation of BIaaS in other organisations is also discussed. Important data arising from the integration of RMaaS and RAaaS are useful for management and stakeholders of University of Southampton.

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.

scholarly journals Comparative Analysis of Risk Reduction Using PERT/CPM Techniques

2019 ◽  
pp. 144-147
Author(s):  
Suchi Pandey ◽  
Hira Singh Yadav
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Program Evaluation ◽  
Comparative Analysis ◽  
Risk Reduction ◽  
Critical Path ◽  
Simulation Method ◽  
Monte Carlo Simulation Method ◽  
Analysis Method ◽  
The Cost

This paper analyzed the traditional probability analysis method for duration risk in program evaluation and review technique (PERT) and Critical Path Method (CPM). On the basis of that it simulates the project’s duration and analyzes the risk by Monte Carlo simulation method. The PERT/CPM produce begins with the hard work of developing an estimate of the cost each activity when it is performed in the planning way (including any crashing).

The Utility of Personnel Selection Decisions

2018 ◽  
Vol 17 (4) ◽  
pp. 172-182 ◽  
Author(s):  
Jisoo Ock ◽  
Frederick L. Oswald
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Model Selection ◽  
Personnel Selection ◽  
Selection Models ◽  
Compensatory Model ◽  
Hurdle Models ◽  
Selection Decisions ◽  
Cost Efficient ◽  
The Cost

Abstract. Compensatory selection is generally more reliable than multiple-hurdle selection. Yet, practitioners may lean toward multiple-hurdle models, because administering an entire predictor battery to every applicant can be time-consuming, labor-intensive, and costly. Using Monte Carlo simulation, we considered some specific cases to illustrate, in terms of selection utility and the cost-reliability tradeoff between compensatory and multiple-hurdle selection models. Results showed that compensatory model selection produced a higher level of expected criterion performance in the selected applicant subgroup, and a higher overall selection utility in most conditions. The simulation provides researchers and practitioners with a practical illustration of the tradeoff between reliable (compensatory) versus cost-efficient (multiple-hurdle) selection models – one that can inspire the exploration of other scenarios and tradeoffs.

Life Cost-Based FMEA Incorporating Data Uncertainty

2002 ◽  
Author(s):  
Seung J. Rhee ◽  
Kosuke Ishii
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle Cost ◽  
Failure Modes ◽  
Design Tool ◽  
Point Estimation ◽  
Data Uncertainty ◽  
Design Alternatives ◽  
Model Complex ◽  
The Cost

Failure Modes and Effects Analysis (FMEA) is a design tool that mitigates risks during the design phase before they occur. Although many industries use the current FMEA technique, it has many limitations and problems. Risk is measured in terms of Risk Priority Number (RPN) that is a product of occurrence, severity, and detection difficulty. Measuring severity and detection difficulty is very subjective and with no universal scale. RPN is also a product of ordinal variables, which is not meaningful as a proper measure. This paper addresses these shortcomings and introduces a new methodology, Life Cost-Based FMEA, which measures risk in terms of cost. The ambiguity of detection difficulty and severity is resolved by measuring these in terms of time loss. Life Cost-Based FMEA is useful for comparing and selecting design alternatives that can reduce the overall life cycle cost of a particular system. Next, a Monte Carlo simulation is applied to the Cost-Based FMEA to account for the uncertainties in: detection time, fixing time, occurrence, delay time, down time, and model complex scenarios. This paper compares and contrasts these three different FMEAs: RPN, Life Cost-based point estimation, and Life Cost-Based using Monte Carlo simulation for data uncertainty.

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