Metaheuristic-Driven Optimization for Complex Multidimensional Decision-Making:

Abstract

Effective management accounting procedures necessitate a thorough evaluation of discrepancies and variability in decision-making processes. However, conventional statistical measures often generate misleading results. In this research, we present the Mean Squares of Criteria and Alternatives (MESCA) metaheuristic approach to multifaceted decision-making. MESCA is specially designed to tackle intricate, multidimensional dilemmas, making it exceptionally suitable for scenarios involving conflicting or competing constraints. This, in turn bolsters the confidence levels of decision-makers, ultimately fostering more informed and enhanced decision-making processes. In this case study, we employ a metaheuristic approach to determine the optimal airport location, contrasting it with the established Simultaneous Evaluation of Criteria and Alternatives (SECA) model. Our research follows a deductive, survey-based methodology, with a focus on prioritizing cities within East Azerbaijan province for airport placement. We consider ten indicators, including economic factors and safety standards, to evaluate the suitability of alternative locations. The comparative analysis between the MESCA method and the SECA model accentuates the advantages of metaheuristic approach over the SECA model, offering valuable insights for effective management decisions. Our findings confirm that both approaches concur in identifying the same city as the most favorable alternative based on the established criteria. Moreover, our research demonstrates that the MESCA method surpasses the SECA model in terms of simplicity, flexibility, and cost-effectiveness, crucial considerations in the realm of management accounting. These revelations provide essential guidance to airport specialists and decision-makers when navigating constraints and selecting optimal airport locations.