Resource Library

An optimization model that balances groups of securities in a portfolio.

A portfolio optimization model that applies the Time Series Fit feature to historical stock price data.

This is a standard product mix model, where five product models must be assembled and then tested on either line 1 or line 2. You will find two versions of this model.
1. A product mix optimization model with uncertainty only in product demands.
2. A model that illustrates the real option of abandoning a project at a future time if the project is not doing well.

A capital budgeting model with uncertainty about projects' resource and capital usage and their NPVs.

A multi-day model for determining when cash should be invested or cash should be obtained.

This model plans production over the next six months in the face of uncertain demand. The company produces three products, and these products compete for labor hours. Each month there are 800 regular-time hours available, and up to 150 overtime hours can be used. The hours used must then be allocated to the three products. Each product has its own production rate (items per labor hour).

A manufacturing company that is building a new production facility for the next 15 years must decide how much capacity to build now in the face of future demand uncertainty, where demand in excess of capacity is lost. Future demands are forecast by using @RISK's Time Series Fit tool on historical demand data. There is also uncertainty in the plant cost, the unit production cost, and the unit operating cost.

This is a simplified model of a multistage manufacturing process. Each stage has a number of identical machines, and each machine can produce a random number of items in a fixed period of time. Each stage feeds the next stage. However, any stage from stage 2 on can produce only as many items as it receives, even it has the potential to produce more. The problem is to determine the number of machines at each stage to maximize mean profit, which is the revenue from selling all items produced during the period minus the operating costs of the machines.

This is a supply chain model of the relationship between a manufacturer and its supplier, modeled from the point of view of the supplier. The manufacturer produces several products with random daily demands. These products require several components from the supplier. The supplier produces these components and stores them at the manufacturer's site so that they are on hand when needed. In turn, the components require several subcomponents. These subcomponents are stored at the supplier's location for use in the components. Production of components and subcomponents is driven by two sets of decision variables, triggers and batch sizes, and the model explores the effects of these.

This model illustrates how disruptions at suppliers, such as weather, strikes, or others, can affect a supply chain, and how such disruptions can be mitigated. The model has two suppliers and two manufacturers. Normally, each supplier supplies a single manufacturer. However, there are occasional disruptions at the suppliers, and each disruption can last a random number of weeks. If an order is placed during a disruption period, this order is ignored by the supplier, resulting in an increased chance of stockouts at the manufacturer. The model explores the mitigation strategy where the manufacturers can "share" suppliers.

This model finds optimal allocations of work hours in several work centers toward production of several SKUs in a specific month. There are four sources of uncertainty: monthly demands for the SKUs, available work hours per day at the work centers, productivities of the work centers for each of the SKUs, and profit margins for each of the SKUs produced at each of the work centers.
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This model finds the optimal numbers of ads for a company to place in various media to minimize the mean cost per exposure. There are two sources of uncertainty for each media: the total audience reached and the percentage of this audience that is the target for the company.