MONTE CARLO SIMULATION OF PROJECT DURATION ASSIGNMENT SAMPLE
Qualitative Risk Estimation
Risk on the exceeding value of the project
As per project wise in this Tesla car project we can analyze the Monte Carlo Simulation by splitting it into six categories:
- The field radiations for electromagnetic.
- Failures occur in System
- Security breaches
- The rates of Accident
- Disclosure for the locations in real time.
- And most importantly the period of time taken for considering all the major factors and completing within the deadline.
On the basis of obtaining the data graph for project duration distribution in days here drawn (Puppo, et al, 2021).
Simulation on decisions on economic factor also be applied to all the problem grades including operate the rules, and the procedures.
Quantitative Risk Estimation
Asymmetrical that represents a slight expansion of the distributed right. For the period of time accordance on the basis of those technical facts the flattening here represents a minimal flattening in compare to the normally distribution.
Figure 1: The Data values are binned, data are in next figures
(source: Provided)
Probability of the project duration will be longer than predicted. Considering Risk acceptance, The strategies for avoiding the risks, mitigation and outsource of the risk the recommendations of calculated periods came (Liu, et al, 2021). The network constructed for all the tasks in the dependence of deviation and the mean and considering the paths presented.
Figure 2: The Networking for the Path Distribution of all the tasks
(Source: Provided)
For the paths which are considered through the task allotment and risk factors, mathematicised and presented in the excel sheet like.
Figure 3: Dataset for required time periods
(Source: Provided)
Figure 4: Continuation of figure 3
(Source: Provided)
Figure 5: Continuation of Figure 4
(Source: Provided)
Comparing Qualitative and Quantitative Risk Assessments
In the terms for exceeding project risk we are using the model for execution period. Here comparatively the model was presented like in first step there is preparation, in second step There is needed to do mean and standard deviation of the tasks of whole work (Chen, et al, 2021). In step 3 tender release and the construction methods, And at the last step we have done testing, operation of commission and towards the completion within the deadline. Here is the short introduction of variable taken randomly in the distribution of triangular in accordance to our excel formula = (Maximum Value – Minimum Value)* RAND () + The Minimum Value…
Figure 6: Triangular Distribution for Min. and Max Val.
(Source: academic.net)
Now if we compare the electromagnetic radiation field with the system and the security breaches for the mechanism of car then we can plot two variations as x and y axis respectively as Quantity evidence and quality evidence. Then many factors coming automatically with the compilation of duration of time. According to that factor here the figure 2 taken.
Figure 7: Safety Breach, Rate of Accident including Duration taken Comparison
(Source: saferemr.com)
Then in then consideration of higher rate of accidents versus the disclosure of real time we can do a vast study in this comparison (Huang, et al, 2021). From the excel dataset plotting of the graph significantly arise here for the Monte Carlo synthesis.
Figure 8: Synthesis of the simulation
(Source: www.journals.plos.org)
On the basis of result the simulation and also how the part concludes can be done. The average of the executed period is varies here is like 180 days but the deadline is within 162 days.
Figure 9: Task deviation through Mean and SD
(Source: Provided)
Using the technique of simulation involving all the algorithmic interactions and the predetermined measures to achieving the object through the analysis of structure presente4d here.
Figure 10: Duration Distribution for Project plotting
(Source: Provided)
Reference list
Journals
Chen, L., Lu, Q., Li, S., He, W. and Yang, J., 2021. Bayesian Monte Carlo simulation-driven approach for construction schedule risk inference. Journal of Management in Engineering, 37(2).
Huang, J., Wu, Y., Sun, J., Li, X., Geng, X., Zhao, M., Sun, T. and Fan, Z., 2021. Health risk assessment of heavy metal (loid) s in park soils of the largest megacity in China by using Monte Carlo simulation coupled with Positive matrix factorization model. Journal of Hazardous Materials, 415, p.125629.
Liu, X., Zhang, J., Yin, J., Bi, S., Eisenbach, M. and Wang, Y., 2021. Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: A data-driven approach. Computational Materials Science, 187, p.110135.
Puppo, L., Pedroni, N., Bersano, A., Di Maio, F., Bertani, C. and Zio, E., 2021. Failure identification in a nuclear passive safety system by Monte Carlo simulation with adaptive Kriging. Nuclear Engineering and Design, 380, p.111308.
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