Aircraft Acquisition Post-Pandemic: Human vs. AI Perspectives using Multi-Criteria Decision Methods

In the post-pandemic era, Indonesia’s commercial airlines are under increasing pressure to expand their fleets in response to a sharp rebound in passenger demand. While traditional aircraft acquisition decisions have relied heavily on expert judgment, recent advancements in artificial intelligence (AI) and decision support systems have introduced new possibilities for enhancing strategic evaluations. This study contributes to the growing body of research on AI-assisted decision-making by comparing human expert assessments with AI-generated recommendations in selecting new aircraft. Using a hybrid multi-criteria decision-making (MCDM) framework that integrates the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we assess eight aircraft models across six key criteria: aircraft price, seating capacity, maximum take-off weight (MTOW), cargo capacity, range, and cost per available seat mile (CASM). Our findings reveal subtle differences in how humans and AI assign weights to each criterion. However, a Mann-Whitney U test (p = 0.689) confirms that these differences are not statistically significant. Notably, both approaches converge on the same optimal choice—the A321neo—highlighting the potential of AI to augment, rather than replace, human decision-making in complex procurement scenarios.

Abstract licence: CC BY

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