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.

Assessing the performance of airlines is vital in the aviation industry, as it affects multiple stakeholders, including airlines, travelers, regulatory authorities, and investors. It is known as a key driver of growth and sustainability in the aviation sector. Hence, the main aim of the current study is to utilize the Principal Component Analysis (PCA) through Q-methodology within the Neutral Cross Efficiency Method (referred to as QNCEM) as an innovative technique to provide an assessment framework for airlines. QNCEM offers policymakers numerous advantages as it permits the elimination of irrelevant perspectives during the assessment process, enables the determination of each Decision-Maker’s (DM) contribution, and plays a crucial role in achieving consensus by leveraging factor analysis to extract perspectives that are representative of the group’s opinions. In this research, the efficiency of 17 Iranian airlines is assessed using QNCEM, considering both desirable and undesirable outputs, such as flight delays, demonstrating its practicality and effectiveness. The selection of a loading factor of 0.626 allowed QNCEM to encompass a comprehensive range of viewpoints from 17 DMs. This deliberate choice ensures the inclusion of a diverse set of perspectives, maximizing the richness of the analysis and explaining a cumulative variance of more than 96%.

The Supplier Selection Problem (SSP) plays a significant role in Supply Chain Management (SCM) in today’s competitive world. With respect to this, the literature reveals that incorporating the viability concept in the SSP for the Oil and Gas (O&G) industry has not been adequately addressed in prior studies. Hence, the current study focuses on the SSP for the energy sector by considering the viability pillars. To do so, a data-driven decision-making model is developed that calculates the weights of indicators executing the Fuzzy Best-Worst Method (FBWM) and then evaluates the performance of the supplier by integrating Data Envelopment Analysis (DEA), Support Vector Machine (SVM), and Random Forest (RF) techniques. Overall, the main contribution of this research is to develop an effective data-driven model to examine the viable SSP for the O&G industry. According to the results obtained, among the potential indicators, cost, quality, responsiveness, manufacturing flexibility, robustness, restorative capacity, pollution control, Waste Management (WM), technical capability, and smart factory are selected as the most significant indicators in their corresponding aspects. Moreover, the comparison results against the classic methods demonstrate the robustness, applicability, and validity of the developed data-driven decision framework. Finally, theoretical and managerial implications are presented.