This page has examples of what you can do with Simulation Master. There is great flexibility in developing and simulating models for a wide variety of applications. The applications below range from project valuation, schedule risk, engineering design and optimization, queuing theory, purchasing decisions, and integration of multi-dimensional integrals.

This is just a small sample of the potential applications to get you started in thinking how random variables can be handled with Monte Carlo simulations.

## Project Business Case

This example simulates a model for a project business case. We want to find out what is the probability that the project will have a positive net present value.

## Project Critical Path Analysis

Simulation of project duration using a spreadsheet model. For a more detailed explanation refer to this article.

## Analysis of a Dewar Flask Design

The design a of Dewar flask used for down hole electronics in the oil & gas industry is analyzed. The amount of time the flask can be down hole while keeping the electronics below 435 degrees Kelvin is analyzed including confidence values. There is also a comparison to traditional scenario analysis. This is a link to the article.

## Optimization of a Dewar Flask Design

The design of a Dewar flask used for down hole electronics in the oil & gas industry is optimized to meet a down hole time requirement. The requirement is that the flask be able to stay down hole for a minimum of 500 minutes while keeping the internal temperature below 435 K with 95% confidence. We also make use of Simulation Master's optimizer. This is a link to the article.

## Mechanical Tolerance Analysis

A tolerance stack-up analysis of a mechanical assembly. For a detailed explanation refer to this article.

## Integration By Simulation

The triple integral of a function is evaluated by simulation. For a detailed explanation refer to this article.

## Queuing Analysis of a Car Wash

A queuing analysis of waiting time for an in-bay automatic car wash is performed using Monte Carlo simulation. For discussion of the model, refer to this article.

## Finding Optimum Purchase Quantity

A purchased component price per unit is based on the quantity ordered. This application uses discrete decision variables for price and order quantity, and runs multiple simulations to find the lowest total cost over the period of the supply agreement. For a detailed explanation refer to this article.

## Probabilistic Design

A beam design problem is solved using probabilistic design. The beam dimensions are determined assuming yield strength, load, and beam dimensions are random variables. Using Simulation Master's optimizer, nominal beam height and width are determined as well as the probability of failure. This is the link to the article.