The Optimization screen is designed to solve dynamic budget allocation problems using criteria priorities and funding profiles to calculate the benefit / cost ratio of alternative options; and then optimizing to see the impact of these selections to find the best combination of alternatives to fund in order to get the highest potential return. Alternatives are funded based on a genetic algorithm that will select the best mix of alternatives in order to maximize the portfolio value score (the sum of the scores of the projects that are funded) based on the constraints that have been input into the Allocation.
There are a number of ways a user may choose to balance a portfolio
When choosing to run the optimizer, the algorithm will attempt to determine a arithmetically optimal portfolio. Optimizer will fund/unfund alternatives to get the most portfolio value possible while staying within resource constraints and honoring dependencies.
Will attempt to remove all bottlenecks for each pool, time period and in total (ie all green).
Will never allocate more resources than are available for a given pool or time period
Will honor any locked alternative (either funded or unfunded)
Optimizer can choose from existing profiles that are checked to include as Optimizer inputs and select the optimal profile for the given scenario
Optimizer will determine optimal sequencing of funded alternatives
Cost Profiles can be moved between all time periods without altering overall magnitude or partial funding
Optimizer will determine optimal pool level breakouts when only an alternative level total (non-designated) is provided