AI-optimised prioritisation of investment in public infrastructure
Public infrastructure is not a ranking problem, but a portfolio decision subject to strict constraints: limited budget, regional balance, equity requirements and environmental impacts. As soon as several projects are under consideration simultaneously , a combinatorial decision space emerges. The key question is: Which combination of projects delivers the maximum societal impact – within the political and operational mandates?
Objective
Maximisation of societal benefit (e.g. avoided disease burden/DALYs, jobs created, regional resilience) within national budget constraints and equity requirements.
Assessment Inputs
- Cost per project
- Economic ROI / Impact (%)
- Equity Index (benefits for low-income groups)
- Environmental Impact Score
Evaluation and selection mechanism
Each project i is assigned a utility value via a multi-criteria utility function:
Uᵢ = w₁ · ROIᵢ + w₂ · Equityᵢ − w₃ · EnvImpactᵢ
Subsequently, it is not ‘the best individual project’ that is selected, but the best combination. StratePlan calculates the portfolio selection in such a way that the sum of the utility values is maximised and all constraints are satisfied.
Constraints
- Total budget : ≤ 500 million USD
- Regional balance : at least 1 project per province
- Equity mandate : minimum value for the average equity score across the entire portfolio
Result
- Optimal project combination with maximum total benefit within budget and mandate requirements
- Transparent derivation: weights, benefit contributions, trade-offs (ROI / impact vs. equity vs. environment)
- Comprehensible documentation for committees, the budget, audit and the public
Technology
StratePlan models the selection as a 0-1 optimisation with constraints (portfolio selection). The impact assessment is structured using EIA-style impact matrices, so that economic, social, equity and environmental criteria can be consistently captured and converted into a decision-ready utility function.
Common Patterns Across Cases
Assessment
Qualitative and quantitative factors are converted into comparable scores – using scales, assessment models or structured expert judgement. The aim is to establish a consistent, decision-ready evaluation basis.
Ranking
Elements are prioritised. However, ranking is rarely the final decision. In complex environments, prioritisation is frequently embedded directly into a combinatorial optimisation process in order to systematically account for interactions and constraints.
Group Selection
The final selection goes beyond a simple ‘Top-k’ approach. StratePlan solves structured selection problems such as knapsack, portfolio or scheduling models and calculates the optimal combination under real-world constraints.
Constraints
Constraints reflect real-world scarcity: capital, time, resources, risk appetite, regulatory requirements, strategic mandates or sustainability requirements. They are an integral part of the decision-making logic.
Technologies
Hybrid use of MCDA methods (e.g. AHP, TOPSIS) for structured evaluation combined with StratePlan for constraint-aware group or portfolio selection.
These cases demonstrate how StratePlan evolves decision-making processes from pure ranking to intelligent, constraint-aware portfolio construction. Evaluation data is translated into actionable, optimised group decisions – aligned with financial, strategic and sustainability-related objectives.
The underlying core logic – structured evaluation → quantitative prioritisation → constrained group selection – scales across different sectors and is adapted in each case to domain-specific success metrics and constraints.
