Research Information System
Engineering Applications of Artificial Intelligence, vol.153, 2025 (SCI-Expanded, Scopus)
Tour operator selection is critical for ensuring high-quality services, customer satisfaction, and sustainable tourism development. However, traditional decision-making methods often fail to address the complexities and uncertainties involved in this process. This study introduces a robust decision-making framework that integrates quantum picture fuzzy rough sets (QPFR) with advanced Multi-Criteria Decision-Making (MCDM) techniques to enhance the evaluation and selection of tour operators. The methodology incorporates QPFR, the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess and rank seven prominent tour operators in the Turkish tourism sector. The evaluation is based on 16 comprehensive criteria: quality, safety, environmental impact, authenticity, and economic contribution. Expert inputs and artificial intelligence techniques were utilized to ensure the model's reliability and accuracy. The findings reveal that the proposed model effectively minimizes uncertainties, provides consistent rankings, and highlights the critical importance of specific criteria in decision-making. Sensitivity analysis confirms the robustness of the results, demonstrating the model's applicability to dynamic and complex decision-making contexts. This study offers theoretical contributions and practical insights for decision-makers, emphasizing the value of integrating advanced computational methods to support sustainable tourism development.