In today's rapidly evolving business landscape, the convergence of sustainable supply chain management (SSCM) and machine intelligence, encompassing artificial intelligence (AI) and machine learning (ML), represents a dynamic and transformative nexus. This comprehensive survey paper navigates the intricate terrain of sustainable supply chain practices, delving into its principles, challenges, and the pressing need for organizations to embrace environmental responsibility, ethical sourcing, and social equity. Simultaneously, it explores the disruptive potential of machine intelligence, offering insights into its underlying principles, vast applications, and its pivotal role in optimizing supply chain operations. Through a systematic analysis, this paper uncovers the complex interplay between SSCM and machine intelligence, starting with the foundational principles of each discipline. It then scrutinizes the challenges encountered in integrating machine intelligence with sustainability, including data complexities, ethical dilemmas, and the need for skilled personnel. Conversely, the paper illuminates the myriad opportunities that arise from this synergy, from enhancing demand forecasting and inventory management to fostering sustainable sourcing practices and reducing waste. In closing, the paper anticipates the future landscape of sustainable supply chains in the age of machine intelligence, highlighting emerging trends, technological innovations, and the ethical considerations that will shape the trajectory of this evolving field. It is our hope that this survey serves as a valuable resource for businesses, policymakers, and researchers alike, inspiring the pursuit of environmentally responsible, economically viable, and ethically sound supply chains in an increasingly interconnected world.

This paper integrated the root assessment method (RAM) with a spherical fuzzy set (SFS) to rank the alternatives and select the best electricity production technology. The SF‐Entropy is used to compute the factor’s weights, and the SF‐RAM method ranks the alternatives. This study used four main factors, 29 subfactors, and 24 alternatives. The four main factors are economic, environmental, social, and political. The results show that economic factors are most important, followed by social, environmental, and political factors. The results of the SF‐RAM method show that hydropower and wind onshore are the highest‐rank technologies in Egypt. The sensitivity and comparative analysis are conducted in this study. The sensitivity analysis was conducted with 30 scenarios in changing factors’ weights and three scenarios in changing of expert’s weight. The sensitivity analysis results show the alternatives’ rank is stable under different scenarios. The comparative analysis was conducted using various multicriteria decision‐making (MCDM) methods. The results of the comparative analysis demonstrate that the proposed methodology outperforms other MCDM approaches. This study supports governments in mitigating environmental impacts, ultimately leading to lower morbidity rates.

The selection and assessment process of appropriate robots became a more complex and complicated task due to various available alternatives and conflicting attributes which must take into consideration. Also, uncertainty which exists usually in the selection process is an unavoidable component that needs to be thoughtfully measured and traditional multi-attribute decision-making approaches failed to deal precisely with it. Since almost all decisions originate from subjective ordinal preferences, handling uncertainty using linguistic variables is also not enough. Thus, the objective of the current study is to present a new extended ordinal priority approach in the neutrosophic environment for the first time to select an appropriate robot. Since neutrosophic is one of the most effective and accommodating tools for handling uncertainty, thus, this method goes to transform linguistic information into triangular neutrosophic numbers using a new presented scale. This scale was used to determine the importance degree of attributes and alternatives regarding experts' opinions. Also, the score function of the triangular neutrosophic number is used for prioritizing attributes and alternatives. The experts in our proposed method have the same degree of importance, since each expert is a person with special skills and knowledge representing mastery of a particular subject. To measure the applicability and efficiency of the proposed approach, an experimental case study has been established for the robot selection problem of a new pharmaceutical city in Egypt for the first time. The source of data in this case study is experts, interviews, and questionnaires. Also, sensitivity and comparative analysis are further made for verifying the power of the proposed approach. The outcome of this study shows that the suggested approach for robot selection is quite helpful and has a great performance under uncertainty over classical and fuzzy ordinal priority approaches. Also, the suggested approach is less consumption of time and simpler than the fuzzy ordinal priority approach. Therefore, we recommend firms and governments to apply it for increasing product quality, hence the profitability of manufacturing industries and decrease needless costs.