This study aims to address the artificial intelligence-based material handling approach selection problem under circular economy to contribute the smart and sustainable business management in logistics systems. The "consensus-reaching process" for experts is not emphasized in the current decision-making procedures with q-rung orthopair fuzzy data. Experts working on group decision-making challenges may hold views that are very dissimilar from one another as a result of their knowledge and experiences. In order for experts to increase the amount of consensus, a consensus-building process is needed. Besides, the ranking results provided by "combined compromise for ideal solution" do not change dramatically in line with the changing weight distributions of characteristics. So, q-rung orthopair fuzzy-based combined compromise for ideal solution methodology with consensus reaching is introduced for solving the addressed emerging problem of logistics companies. This robust and logical decision-making method can comprehensively analyze the advantages, disadvantages, and potential barriers to the acceptance of artificial intelligence-based material handling approaches. The real-life study is offered for a logistics company that plans to invest in robotic solutions based on artificial intelligence. The findings show that autonomous mobile robots represent the best artificial intelligence-based material handling approach. Recommendations for adopting alternative solutions are provided to assist in the efficient completion of smart logistics activities.

Complex Fermatean fuzzy sets (CFFSs) integrate the ideas of complex fuzzy sets and Fermatean fuzzy sets, where the membership, non-membership, and hesitancy degrees are all complex numbers, allowing the express uncertain information more flexibly and comprehensively. However, how to reasonably measure the discrepancies between CFFSs in decision-making remains an open task. This paper presents a series of new distance measures of CFFSs and their weighted versions based on Hamming, Euclidean, Hausdorff, and Hellinger distances. On this basis, we explore some outstanding properties that the proposed measures satisfy (i.e., boundedness, nondegeneracy, symmetry, and triangular inequality) and demonstrate their effectiveness through several examples. Furthermore, we design a decision-making algorithm as well as a clustering algorithm based on the proposed measures and verify the performance of the proposed measures through several applications.