Abstract
Infrastructure investment is central to sustainable economic development, yet governments, particularly in developing countries face persistent challenges in prioritising projects under limited budgets, reduced donor funding, and fragmented governance. Existing frameworks, such as Multi-Criteria Decision Making (MCDM), offer structured evaluations but often rely on subjective weightings and fail to capture trade-offs among financial, social, and technical objectives. This study develops a hybrid data-driven decision-support framework that integrates multi-objective optimisation (MOO) with Multi-Criteria Analysis (MCA) to enhance the transparency, rigour and legitimacy of infrastructure prioritisation.
Methodology
A two-phased methodology was applied. First, NSGSA-III algorithm was used to evaluate 41 infrastructure options against three objectives: minimising long-term costs, maximising social-benefits, and improving feasibility and access. This optimisation generated a Pareto front of efficient projects. Second, MCA was employed to rank these Pareto-optimal options using stakeholder-informed weights derived from pairwise comparison across six factors: social, sustainability, technical, economic, environmental and visual.
Results
The results identified three non-dominated projects, PB01, Road 6+6A, and VB08, as efficient trade-off solutions. PB01 ranked highest in the MCA (~0.64), reflecting its strong social and sustainability outcomes despite higher costs. VB08 (~0.62) provided a balanced compromise, while Road 6+6A (~0.33) was the most affordable but the weakest in terms of technical and economic performance. These findings highlight that optimisation alone cannot determine priorities; stakeholder-driven evaluation is essential to align outcomes with developmental goals.
Conclusion...