Abstract
This study introduces a hybrid decision-making framework to evaluate and prioritize energy retrofit strategies in airport infrastructure, addressing the dual goals of sustainability and operational feasibility. The proposed model integrates the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for generating Pareto-optimal solutions, K-Means clustering for classifying strategies, and the Pythagorean Fuzzy Analytic Hierarchy Process (PFAHP) for prioritizing alternatives under uncertainty. The framework was applied to a representative mid-sized international airport scenario, constrained by a maximum budget of $1 million, implementation timelines of up to 18 months, and an operational disruption threshold of 3 on a 5-point scale. Nine distinct retrofit strategies were identified, with costs ranging from $850,000 to $1,000,000 and energy savings between 20% (250,000 kWh) and 30% (360,000 kWh) annually. Carbon reductions ranged from 15% (approximately 102 metric tons per year) to 30% (around 144 metric tons per year), while implementation times varied from 6.16 to 11.92 months. Disruption levels ranged from minimal (1.23) to moderate (5.00). Among these, Solution 9 achieved the highest overall priority score (0.708), offering 30% energy and carbon savings at a cost of $1,000,000, with an 11.03-month timeline and moderate disruption level (4.09). Cluster analysis grouped solutions into three profiles: low-cost (average cost $859,375, energy savings 20.63%), balanced (average cost $906,250, energy savings 23.75%), and high-impact (average cost $973,750, energy savings up to 30%). Sensitivity analysis further confirmed the robustness of the prioritization, with only minor score fluctuations under adjusted scenarios. These findings provide concrete, actionable guidance for airport decision-makers to support strategic energy retrofit investments aligned with ICAO's CORSIA framework and UN Sustainable Development Goals, enabling tangible progress toward net-zero operations.