Optimization

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Definitive Pro employs a state-of-the-art mathematical programming solver to optimize the allocation of resources.

Mathematical solvers that utilize constraint-based linear programming (LP) are powerful tools for optimizing resource allocation, particularly in complex environments where multiple constraints and objectives must be balanced. Linear programming involves creating mathematical models where the objective function, such as maximizing profit or minimizing cost, is subject to a set of linear constraints representing the limitations on resources, such as budget, manpower, or time. These solvers work by identifying the most efficient way to allocate resources across various competing activities, ensuring that the constraints are satisfied while optimizing the overall objective.

In project portfolio management (PPM), constraint-based linear programming is applied to optimize the selection and scheduling of projects within a portfolio. Given the finite resources available, such as budget, personnel, and time, LP solvers help organizations determine the best combination of projects to pursue in order to maximize overall value, such as ROI, strategic alignment, or risk mitigation. By modeling the portfolio as a linear program, decision-makers can ensure that resources are allocated in a way that meets organizational goals while adhering to budgetary and operational constraints. This method not only enhances efficiency but also supports strategic decision-making by providing clear, data-driven insights into the trade-offs involved in different portfolio configurations.

The optimizer supports functions that can’t be performed manually. It identifies the recommended portfolio of projects that together deliver the greatest strategic benefit, while satisfying the following constraints and dependencies:

• Budget constraints (e.g., the portfolio cannot exceed a maximum cost of $15M)
• Resource constraints (e.g., no resource can be over allocated)
• Planning constraints (e.g., include the “must-haves”, exclude the “must-nots”)
• Dependencies (e.g., two or more alternatives that are mutually exclusive, mutually inclusive, or have a relationship where one requires the other)
• Group constraints (e.g., select 3 or more alternatives from group “A”, select no more than 2 alternatives from group “B”)
• Criteria allocation constraints (e.g., ensure the mix of the portfolio includes a 30% investment level in criteria “A”)
• Partial funding (i.e., alternative “A” can be funded at less than 100% of its budget request)