Procurement Risk Management of Engineering Materials Based on Uncertain Rising Prices

2012 ◽  
Vol 174-177 ◽  
pp. 3219-3222
Author(s):  
Hui Chen
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Calculation Method ◽  
Economic Order Quantity ◽  
Purchasing Management ◽  
Order Quantity ◽  
Management Method ◽  
Engineering Materials ◽  
Procurement Risk

Practical risk purchasing management method of engineering materials is put forward by using Monte Carlo Simulation. First, the calculation method of economic order quantity is put forward based on rising price. Second, appropriate insurance inventory is calculated when material requirements is variable.

scholarly journals Monte Carlo Simulation Approach to Determine the Optimal Solution of Probabilistic Supply Cost

2020 ◽  
Vol 2 (1) ◽  
pp. 1-6
Author(s):  
Helmi Ramadan ◽  
Prana Ugiana Gio ◽  
Elly Rosmaini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Random Process ◽  
Historical Data ◽  
Optimal Solution ◽  
Economic Order Quantity ◽  
Simulation Approach ◽  
Order Quantity ◽  
Probabilistic Simulation ◽  
The Future

Monte Carlo simulation is a probabilistic simulation where the solution of problem is given based on random process. The random process involves a probabilitydistribution from data variable collected based on historical data. The used model is probabilistic Economic Order Quantity Model (EOQ). This model then assumed use Monte Carlo simulation, so that obtained the total of optimal supply cost in the future. Based on data processing, the result of probabilistic EOQ is $486128,19. After simulation using Monte Carlo simulation where the demand data follows normal distribution and it is obtained the total of supply cost is $46116,05 in 23 months later. Whereas the demand data uses Weibull distribution is obtained the total of supply stock is $482301,76. So that, Monte Carlo simulation can calculate the total of optimal supply in the future based on historical demand data.

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 Some Results on Measures of Interaction between Paired Risks

Risks ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 88 ◽  
Author(s):  
Rui Fang ◽  
Xiaohu Li
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Risk Measures ◽  
Dependence Structure ◽  
Marginal Distributions ◽  
Numerical Examples ◽  
Risk Contribution ◽  
Actuarial Risk

Co-risk measures and risk contribution measures have been introduced to evaluate the degree of interaction between paired risks in actuarial risk management. This paper attempts to study the ordering behavior of measures on interaction between paired risks. For various co-risk measures and risk contribution measures, we investigate how the marginal distributions and the dependence structure impact on the level of interaction between paired risks. Also, several numerical examples based on Monte Carlo simulation are presented to illustrate the main findings.

scholarly journals Intergreen Time Calculation Method of Signalized Intersections Based on Safety Reliability Theory: A Monte-Carlo Simulation Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Ze-hao Jiang ◽  
Tao Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Calculation Method ◽  
Traffic Accidents ◽  
Reliability Theory ◽  
Driving Behavior ◽  
Signalized Intersections ◽  
Signal Change ◽  
Conventional Calculation ◽  
Clearance Interval

In China, around ninety percent of the traffic accidents at signalized intersections occur within the signal change intervals, especially during signal change from green to red. Hence, intergreen time (IGT), that is, yellow change interval plus red clearance interval, is of great significance to the safety at signalized intersections. The conventional calculation method of IGT ignores the randomness of drivers’ behaviors, which we believe is an important factor in calculation of IGT. Therefore, the purpose of this research is to investigate a new approach to calculate the IGT based on safety reliability theory. Firstly, a comprehensive literature review concerning the conventional calculation methods of IGT is conducted. Secondly, a theoretical calculation method of IGT based on safety reliability theory is put forward; different from the conventional methods, this model accounts for the uncertainty of driving behavior parameters. Thirdly, a Monte-Carlo simulation is employed to simulate the interactive process of perception-reaction time (PRT) and vehicular deceleration and solve the proposed model. Finally, according to the Monte-Carlo simulation results, the curve clusters describing the relationship between IGT, safety reliability (50%-90%), and intersection width (15-35m) are drawn. Results show that the IGT of a signalized intersection, obeying the normal distribution, is influenced by multiple factors and most sensitive to the PRT and vehicular deceleration. Our method thus successfully incorporates the probabilistic nature of driving behavior. Taking the safety reliability into consideration can provide a more reasonable method to calculate the IGT of signalized intersections.

Analysis on Motion Precision Reliability of Hydraulic Support

2011 ◽  
Vol 48-49 ◽  
pp. 224-227
Author(s):  
Dong Chen Qin ◽  
Qiang Zhu ◽  
Hong Xia Wu ◽  
Zhe Feng Guo
Keyword(s):  
Mathematical Model ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Calculation ◽  
Calculation Method ◽  
Effective Length ◽  
Future Research ◽  
Hydraulic Support ◽  
Continuous Contact ◽  
Continuous Contact Model

In order to research the motion precision reliability of hydraulic support when the influence of the bar length error and gap error is considered, the motion trace mathematical model for the top beam of hydraulic support is established, with the calculation method of motion precision reliability and the effective length of bar based on continuous contact model. Taking some type of hydraulic support as an example, its motion precision reliability is calculated and analyzed. The Monte Carlo simulation is also used to verify the model, and the T-R curve of the gap error and the reliability is plotted. The results from simulation accord with those from the theoretical calculation, which verifies the model established and can provide some valuable reference for the related future research.

The Research of Construction Project Risk Management Based on Monte Carlo Method and Fuzzy Mathematics

2012 ◽  
Vol 442 ◽  
pp. 341-345
Author(s):  
Si Dong Xu ◽  
Xiao Li Cai ◽  
Wei Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Qualitative Analysis ◽  
Quantitative Research ◽  
Construction Project ◽  
Negative Influence ◽  
Fuzzy Mathematics ◽  
Project Risk

A lot of risks of management occur under the process of the construction project, and it will always bring more tremendous negative influence to the project goal. Therefore, there should have the effective risk method for the project superintendent to forecast the risk occurrence and reduce the loss which the risk brings. There are lots of methods about risks management under the process of the construction project at present and most of the method emphasize particularly on qualitative analysis. This article presents a method which base on the knowledge of Monte Carlo Simulation. It will combine the qualitative analysis and quantitative research and puts forward a new solution.

Investment risk management in Tehran Stock Exchange (TSE) using technique of Monte Carlo Simulation (MCS)

2010 ◽  
Vol 17 (2) ◽  
pp. 265-278 ◽  
Author(s):  
Darush Farid ◽  
Alireza Rajabipoor Meybodi ◽  
Seyed Heydar Mirfakhraddiny
Keyword(s):  
Monte Carlo Simulation ◽  
Risk Management ◽  
Monte Carlo ◽  
Stock Exchange ◽  
Investment Risk
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