Aircraft selection in airline industry using Bipolar Picture Fuzzy Numbers with Einstein aggregation operator

Choosing the most suitable aircraft is becoming increasingly challenging because many of the evaluation factors are interlinked and are inherently uncertain. Shifts in the market and rapid advances in technology can change purchasing costs, fuel volume capacity, seating arrangements, operating range, maximum gross (take-off) weight, and cargo capacity, making precise assessment challenging. As a result, conventional decision-making methods can often fall short of delivering dependable results under these conditions. To overcome this challenge, the present work introduces a fuzzy multi-criteria decision-making approach. In the proposed model, fuzzy SWARA procedure is used to derive the relative importance of the criteria based on expert opinions, which are represented as Bipolar Picture Fuzzy Numbers (BPFNs). These expert judgements are combined through the Einstein aggregation operator, and the derived weights are applied within the MARCOS method to rank the available aircraft alternatives. The findings demonstrate that the proposed framework is both stable and consistent, and they highlight its potential for broader decision-making tasks that involve uncertainty across various industries.