scholarly journals Dynamic Cooperative Model for Ranking Construction Risks using Monte Carlo Simulation

2020 ◽  
Vol 9 (12) ◽  
pp. 211-216
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Construction Projects ◽  
Critical Path ◽  
Quantitative Model ◽  
Decision Makers ◽  
Path Model ◽  
Simulation Engine ◽  
Construction Risks ◽  
Risk Planning

Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers' selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project's activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level.The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way.

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.

Risk Management of Construction Schedule by PERT with Monte Carlo Simulation

2014 ◽  
Vol 548-549 ◽  
pp. 1646-1650 ◽  
Author(s):  
Yang Liu ◽  
Yan Li
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Critical Path ◽  
Total Duration ◽  
Control Points ◽  
Schedule Management ◽  
Simulation Results ◽  
Critical Paths ◽  
Construction Schedule

It has been proved that the construction schedule management was an uncertain problem. Traditional CPM method was a good way to define the total duration and critical paths but can not solve uncertainty. The paper use CPM to define the duration and critical path firstly, then defined the parameters with Delphi and make Monte Carlo simulation. Through simulation results, it is found that the probability to finish the work on time was only 35.3%. The following step is to make sensitivity analysis, through the calculation, the work which has large influence was found and treat as key control points. It is proved that Monte Carlo simulation is useful to solve the problem of construction schedule management.

scholarly journals Analysis of modern construction projects using montecarlo simulation technique

2018 ◽  
Vol 7 (2.19) ◽  
pp. 41
Author(s):  
A. M. Arunmohan ◽  
M. Lakshmi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Quantitative Analysis ◽  
Construction Projects ◽  
Project Success ◽  
Risk Identification ◽  
Project Schedule ◽  
Montecarlo Simulation ◽  
Total Project Cost ◽  
Total Project

Today, Construction based Industry is the prospering industry which has a high economical influence on any nation. Delay in the huge construction project increases the total project cost. Henceforth, uncertainties as well as risks must be significantly regarded during the project. For organizing and completing the projects in a financially, timely and qualitatively accountable manner, careful scheduling of projects is compulsory. Effectual scheduling of project assures project success. This study concentrates on qualitative analysis, risk identification, together with quantitative analysis. The targets are i) to ascertain the key risk aspects that disturb the project schedule, and ii) to find the probability of finishing the project within specified time. Questionnaires are distributed amongst 20 industry practitioners with disparate experience from [1] to [25] years.  Quantitative analysis is made by the methods like Monte Carlo simulation (MCS) and PERT. @RISK by Palisade corp. is utilized for MCS.

Spiegel Online

2017 ◽  
pp. 1-11
Author(s):  
Karl Schmedders ◽  
Armin Rott
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Web Site ◽  
Business Plan ◽  
Decision Makers ◽  
Bottom Line ◽  
Art Objects ◽  
Input Parameters ◽  
High Level ◽  
The Impact

Spiegel Online (www.spiegel.de) is the leading news Web site in Germany. The site was first designed to accompany Der Spiegel, one of Europe's largest and Germany's most influential weekly magazine, which has a weekly circulation of around one million. The site's content is produced by a team of more than fifty journalists writing in several categories: politics, business, networld, panorama, arts and entertainment, science, university, school, sports, travel, weather, and automobiles. The original content is complemented by articles purchased from news agencies and selected articles from the print edition. Spiegel-Verlag is a major contributor to the Hamburg Media School, which offers professional master's degree programs in Media Management (MBA), film, and journalism. In their second year, MBA students typically engage in consulting projects with major media companies. In a recent assignment, Spiegel Online posed two questions to the MBA team: are there any chances for an economically successful entry into the market for interactive classifieds? And if so, what should the business model look like in detail? A student team analyzed markets for classified ads and found one market segment that appeared to be particularly promising: the market for art objects. During the development of a business plan for a new venture in this market it became apparent that there is much uncertainty about the key input parameters to the business plan. As a result, it is very difficult to assess the viability of the business idea. How can the team properly account for the uncertain input parameters? What is the impact of this uncertainty on the bottom line? Will a Web site for art objects earn or lose money? How can the team communicate this uncertainty to a group of high-level decision makers who want a simple “go or no-go” recommendation?The objective is to make students aware of the applicability of Monte Carlo simulation to the analysis of complex business plans. Students should learn how to explicitly account for uncertain inputs in a business plan, how to assess the impact of uncertainty on the bottom line via Monte Carlo simulation, and how to communicate the results of their analysis to high-level decision makers.

Monte Carlo Simulation–Based Assessment of Risks Associated with Public–Private Partnership Investments in Toll Highway Infrastructure

2017 ◽  
Vol 2670 (1) ◽  
pp. 59-67 ◽  
Author(s):  
Zhe Han ◽  
Juan Diego Porras-Alvarado ◽  
Jingran Sun ◽  
Zhanmin Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Investment Decision ◽  
Quantitative Information ◽  
Decision Makers ◽  
Control Measures ◽  
Highway Projects ◽  
Monte Carlo Simulation Technique ◽  
Funding Mechanism ◽  
Private And Public

The demands for delivering highway services keep growing worldwide. However, funding from government and public agencies alone cannot cover the capital needed to operate and maintain existing highway systems, much less to construct new ones. Public–private partnerships (PPPs) are an innovative funding mechanism for highway agencies to use private capital and expertise in transportation infrastructure projects so as to increase funding options to bridge the budget gap. Even though parties involved in PPPs take different roles and responsibilities, there are still risks taken or shared by the public and private sectors. In particular, assessing risks associated with the potential returns of investments is of great importance to the private and public sectors. This paper presents a methodological framework for assessing the investment risks of PPP toll highway projects, which may help decision makers. The financial viability associated with the components of a project is considered and analyzed, and the Monte Carlo simulation technique is applied to evaluate the overall project risks. Finally, a numerical case study is conducted to demonstrate the application of the proposed method. The risk analysis provides statistical distribution of investment returns for the project under analysis, which will supply decision makers with direct information to estimate the project’s overall financial risks and develop corresponding risk control measures. The risk simulation results are interpreted so that quantitative information can be provided to agencies to establish investment decision criteria.

Quantification, Optimization and Uncertainty Modeling in Information Security Risks

2010 ◽  
Vol 23 (2) ◽  
pp. 33-52 ◽  
Author(s):  
Sanjay Goel ◽  
Eitel J.M. Lauría
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Modeling ◽  
Quantitative Model ◽  
Government Agency ◽  
The Other ◽  
Risk Exposure ◽  
Security Risk ◽  
Security Risks

In this paper, the authors present a quantitative model for estimating security risk exposure for a firm. The model includes a formulation for the optimization of controls as well as determining sensitivity of the exposure of assets to different threats. The model uses a series of matrices to organize the data as groups of assets, vulnerabilities, threats, and controls. The matrices are then linked such that data is aggregated in each matrix and cascaded across the other matrices. The computations are reversible and transparent allowing analysts to answer what-if questions on the data. The exposure formulation is based on the Annualized Loss Expectancy (ALE) model, and uncertainties in the data are captured via Monte Carlo simulation. A mock case study based on a government agency is used to illustrate this methodology.

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