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.

The evaluation of autonomous urban freight logistics (UFL) solutions is crucial due to the increasing need for efficient and sustainable transportation systems in urban areas. Optimizing UFL possibilities becomes essential for developing smart city projects as cities expand and the need for reliable logistical services increases. This study introduces an artificial intelligence (AI)-driven framework for group decision-making in UFL evaluation. It develops single-valued neutrosophic (SVN) Copula-Dombi averaging and geometric operators that act as AI reasoning tools, capable of handling uncertainty, contradiction, and indeterminacy beyond classical models. To improve reliability and agreement among decision-makers, the study also presents a consensus-based SVN Copula-Dombi multiple triangles scenarios (MUTRISS) decision support model. The model is tested on a real case in India. It evaluates drones, electric light commercial vehicles (e-LCVs), autonomous e-LCVs, and droids as UFL solutions. Twelve criteria are used, and their weights are set with an optimization model. Results show drones rank first with a score of 0.6055, followed by droids at 0.6033. The study also checks robustness through comparison and sensitivity tests. The study supports smart city logistics and sustainable urban development by assisting logistics managers and city authorities in selecting appropriate autonomous UFL systems.