Resources
In many circumstances one wishes to calculate the aggregate impact of many possible yes/no type events. For example, it is often important to answer questions such as "What is the loss amount that will not be exceeded in 95% of cases?" Simulation is usually required to answer such questions. In this model, the "yes/no" events are modeled using Binomial distributions. The results profile shows a multi-peaked distribution, which is typical when there are discrete-type inputs.
The purpose of this model is to see how many hospitals are required to accommodate all patients in various scenarios. The model assumes that patients are assigned to hospitals in a particular order: hospital 1, then hospital 2, and so on. There is uncertainty in the numbers of available beds at the hospitals.
This example illustrates a general multiserver queueing system. Customers arrive at random times. If at least one of the servers is idle, an arrival goes directly into service. But if all servers are busy, the arrival joins the end of a queue, from which customers are served in first-come-first-served order.
This example adapts the general multiserver queueing system to a city's ambulance service. The ambulances are the "servers" and a customer "arrival"; corresponds to a call for an ambulance from some location. If at least one ambulance is not currently busy, it responds immediately to the call for service. Otherwise this call waits, in first-come-first served order, for the next available ambulance. In addition to the usual queueing outputs, the model keeps track of waiting and ambulance costs per day. The number of ambulances can be increased or decreased to explore the trade-off between ambulances and waiting time.
A set of basic @RISK models for modeling events:
1. Any (or all) events could occur.
2. A version illustrating the RiskMakeInput function for use in sensitivity analysis.
3. A version where dependent events can occur (or not occur) sequentially in time.
A model that illustrates one possible generic approach for generating demands for substitute products.
An inventory ordering model that illustrates how the Time Series Define feature can be used to generate demands.
A model which illustrates how the bullwhip effect can occur with a single retailer and supplier and a second model that illustrates the bullwhip effect when there are multiple tiers of suppliers.
This model of a company's multiple projects over a 12-month horizon. Each project is planned to start in a given month, and from that month on, it has anticipated costs, some of which are known (or 0) and some of which are uncertain. There are possible random delays (or, for a few projects, possible earlier starting months), which shift the cost schedule to the right (or the left). In addition, each project has a 5% chance of failing in any month after its actual starting month. If it fails in a given month, that month's costs plus any remaining months' costs are not incurred._x000D_
A model that illustrates how @RISK can be used to correlate task times in a project.
A model for determining whether the total of uncertain costs meets a budget
A model of an electrical system that illustrates @RISK's Six Sigma functions.
