Managing the Balance Between Project Value and Net Present Value Using Reinforcement Learning

Project managers make decisions weighing financial returns (net present value, NPV) and value creation expected by stakeholders. Often, plans maximizing NPV neglect stakeholder benefits while those focused strictly on value creation may reduce financial viability. This paper puts forth a new stochastic optimization model handling this compromise using a mixed integer program solved with reinforcement learning. The model incorporates uncertain activity durations and considers positive and negative cash flows. Our Monte Carlo control method with
$\epsilon $
-greedy policies and timed start actions for activities facilitates the simultaneous maximization of NPV and project value. The resulting efficient frontier delineates various project plans, demonstrating the trade-off between maximizing NPV and project value, providing decision makers with visual analysis to select plans that fit organizational needs. Computational experiments demonstrate superior performance over a mathematical solver limited by the problem’s complexity and a metaheuristic lacking guided online learning. The results help senior management select satisfactory plans that balance financial returns with stakeholder preferences. The methodology contributes a novel tool for quantitatively incorporating value creation alongside financial objectives in project planning.