AI-driven Optimization of project portfolios in corporate ecosystems with synergies and strategic factors

• Innovative synergy-driven optimization framework: introduces a hybrid framework combining Pyomo-Gurobi optimization and XGBoost machine learning to dynamically incorporate synergy effects in Multi-Objective Portfolio Optimization (MOPO). • Enhanced decision-making for CEOs: provides tools for quantifying trade-offs among strategic objectives using Pareto curves, enabling CEOs to balance risk, return, and strategic priorities in corporate ecosystem creation. • Strategic integration of synergies: demonstrates how AI-driven synergy predictions improve portfolio performance by capturing dynamic interdependencies between projects, offering a competitive edge in ecosystem management. • Actionable insights from computational experiments: validates the framework through computational experiments, showing that synergy-optimized portfolios outperform traditional models in terms of risk-adjusted returns. • Open-source implementation for broader impact: includes detailed code and experimental setups on GitHub, ensuring reproducibility and enabling researchers and practitioners to leverage and extend the methodology. • Strategic alignment with sustainability goals: integrates desirability, feasibility, and sustainability dimensions into portfolio optimization, aligning decision-making with broader strategic and societal goals. This paper studies the optimization of project portfolios in corporate ecosystems by considering both strategic factors and return synergies between projects. We propose a hybrid method that combines machine learning with mathematical programming to address this enhanced form of project portfolio optimization. Unlike traditional approaches, which evaluate projects mainly based on individual risks and returns, our framework considers strategic priorities and the extra value created when projects reinforce each other. Machine learning models predict synergies, while exact optimization ensures consistent portfolio selection under resource and strategic constraints. A numerical proof-of-concept illustrates the methodology. Computational experiments show that portfolios designed with synergy and strategy in mind might achieve a significantly higher performance than portfolios that do not account for project synergies. The paper also examines computational efficiency and scalability, highlighting the approach’s potential for practical application in complex and dynamic corporate ecosystems.