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Optimizing Your Portfolios in Optsee®

Optsee® allows you to study the behavior of portfolios under multiple parameters, and displays charts and lists of projects ranked in order of Overall Value. This information alone is often not adequate to easily select an optimized set of projects based on business constraints such as budget dollars, available human resources, acceptable risk, timing, etc. For example, you may have 20 potential projects that would cost a sum total of $40 million and would require 30 full-time employees to complete. If you only have a budget of $20 million and 22 full-time employees, it can be very difficult and time consuming to manually determine the best selection of projects to resource. In this 20-project example, there are over 1 million different possible portfolios. In a 30-project portfolio, there are over 1 billion different possible portfolios, and the number continues to rise exponentially as shown in the chart below:

An optimization finds optimal or close to optimal portfolios based on your constraints from many potential solutions. The surface map below shows all the possible solutions meeting limited cost and limited employee constraints for a small (12 project) portfolio. This portfolio has 4,096 potential solutions and an exhaustive search of all possible solutions (a "brute force" search) identified 2,773 solutions. Of these 2,773 solutions, three optimal solutions were can be seen among many local smaller maxima.

Even though this was a small portfolio with only two constraints that was easy to solve using the "brute force" technique, it illustrates the challenges of optimizing larger portfolios with multiple constraints and billions of potential solutions :

  • The optimizer must be able to "explore" the optimization surface to find potential optimal solutions
  • The optimizer must avoid converging prematurely on a maxima that is not close to optimal
  • The optimizer must be able to find optimal solutions in a reasonable time period

Optsee® Pro provides you with three different built-in optimizers:

  1. The "Brute Force" optimizer: Use this optimizer to find an optimal solution for portfolios consisting of not more than 20 Projects (1,048,576 possible solutions).
  2. The "Genetic Optimizer": This optimizer uses a proprietary genetic algorithm to find an optimized set of Projects that satisfy up to thirty different constraints.
  3. The "Branch and Bound" optimizer: This is a proprietary “Branch and Bound” algorithm designed to quickly explore the optimization surface and find optimal solutions if one exists. It is designed for portfolios containing less than 32 projects. IMPORTANT NOTE: The “Branch and Bound” Optimizer IS DISABLED for finding optimized portfolios using only "Mean not greater" or "Mean not less" constraints as it is not effective for these kinds of optimizations.

Optsee® Plus includes only the "Branch and Bound" optimizer, and does not have the capability to optimize using dependency and "Force-In" and "Force-Out" constraints.

The selection of which optimizer to use depends on the particular optimization challenge. Optimizations of large portfolios with large numbers of constraints or tight constraints take longer than smaller portfolios with looser constraints. Optimizing using Mean value constraints takes longer. We generally recommend testing several optimizer and optimization parameters to determine which optimizer and settings give you the best performance and results for your particular portfolio/constraint combination.

You can also use the [Suggested Parameters] button at the bottom of the Optimization form as a starting point for your portfolio.

1. Start by clicking the [Show Optims] button at the top of the Portfolio form.

2. If the portfolio has never been optimized, this will open an empty optimizer list at the bottom of the portfolio form. At the bottom of the optimizer list are three buttons; click the [New Optimizer] to open the optimizer form.

The example above has 3 constraints. It will find a subset of projects that maximizes the total portfolio SMART Score while not exceeding $70,000 in “Cost ($),” 65 “Resources Required,” and keeping the mean of the “Probability of Success (%)” for the entire portfolio above 65%.

3. The Optimizer progress form opens to show you the progress of the optimization. The optimizer works by using a carefully-designed algorithm that tests partial portfolio subsets and quickly discards those that dont meet your constraints. This way, it finds an optimized portfolio without having to check all the possibilities.

4. When the optimization is complete, the results are displayed in the Optimization Results form. This optimization yielded portfolio with a Total SMART Score of 650.64. It spend $66,930 of the $70,000 cost constraint,; it uses 64 of the 65 required resources constraint, and the mean probability of success for the selected projects is 71.94%.

It took less than a second to find this portfolio out of over 1 million possible portfolios.

5. After the optimization, the selected projects are marked with a checkmark in the “Last” column in the portfolio form. Because this is the first optimization, the “Base” column as the same checkmarks. (As you will see later, these columns let you compare different optimizations.) By highlighting the selected projects and clicking the “Subset” button, we can display only the 16 selected projects in the form. The data at the bottom provides a statistical summary of each attribute column. The highlighted fields correspond to constraint values displayed in the Optimization Results form.

6. After running 2 more optimizations with lower cost and resource constraints, we obtained the results shown below.

7. You can compare different optimizations in the portfolio form by checking the “Base” and “Last” columns in the Optimizer list form at the bottom of the Portfolio form. The results of the optimization selected as the “Base” optimization appear in the “Base” column and the results of the “Last” optimization are displayed in the “Last” column. A red symbol color indicates a difference between the two optimizations and black indicates the same status.

Next: Optimizing Using Project Dependencies