The Projects can be modeled as a portfolio and optimized to determine the best combination of projects for the portfolio in the Optimization Settings tab (Figure 8.1 shows the Optimization Settings of the DCF default example model after the Simulate All Options at Once selection was run in the Risk Simulation section). Select the decision variable type of Discrete Binary (chooses which Projects to execute with a Go/No-Go binary 1/0 decision) or Continuous Budget Allocation (returns % of budget to allocate to each Project as long as the total portfolio is 100%); select the Objective (e.g., Max NPV, Min Risk, etc.); set up any Constraints (e.g., budget or number of projects restrictions, or create your own customized restrictions); then select the Projects to optimize/allocate/choose (default selection is all Projects); and when completed, click Run Optimization. The software will then take you to the Optimization Results (Figure 8.1).
There are also some additional advanced capabilities in this settings tab. For instance, the Compare Models button allows you to select and run multiple saved optimization models to compare the results. This is useful if you wish to compare results side by side, multiple optimizations with different objectives. The Custom Objective and Custom Constraints allow you to create your own user-specified variables. Enumeration will run if there are less than a dozen decision variables and discrete go/no-go decisions are selected, where every possible combination and permutation will be tested, thereby taking a little longer to run than usual, but will guarantee a global optimum result. The Advanced Settings button allows you to set the precision of the solution, maximum number of iterations, runtime, phased optimization, as well as sequential starting decision variables when building the efficient frontier. Finally, you can use previously saved results to run the optimization or load and use the latest results based on the simulation you have just run, or manually override the inputs.
Decision variables are quantities over which you have control, for example, the amount of a product to make, the number of dollars to allocate among different investments, or which projects to select from among a limited set. As an example, portfolio optimization analysis includes a go or no-go decision on particular projects. In addition, the dollar or percentage budget allocation across multiple projects also can be structured as decision variables.
Constraints describe relationships among decision variables that restrict the values of the decision variables. For example, a constraint might ensure that the total amount of money allocated among various investments cannot exceed a specified amount or at most one project from a certain group can be selected; budget constraints; timing restrictions; minimum returns; or risk tolerance levels.
Objectives give a mathematical representation of the model’s desired outcome, such as maximizing profit or minimizing cost, in terms of the decision variables. In financial analysis, for example, the objective may be to maximize returns while minimizing risks (maximizing the Sharpe’s ratio or returns-to-risk ratio).