Sustainable and resilient supplier selection and management are essential to building environmentally responsible and resilient supply chains. Selecting sustainable and resilient suppliers enables organizations to ensure the long-term viability of the supply chain. To do that, there is a need to identify suitable suppliers that align with sustainability and resilience goals. This study aims to provide an overview of the requirements and criteria for selecting and managing sustainable and resilient suppliers and emphasizes the significance of integrating sustainability and resilience principles throughout the supply chain. Multi-criteria decision-making (MCDM) is used to deal with various criteria. The double normalization-based multi-aggregation (DNMA) method ranks the alternatives. This study used 18 criteria and 12 alternatives to select the best one. By employing these criteria, organizations can identify suppliers that align with sustainability and resilience goals. The results show that alternative 3 is the best and alternative 11 is the worst. Also, the results show that the proposed method can provide a new method to rank alternatives. Moreover, the proposed method can introduce a more simple and flexible method for selecting a suitable supplier and ensuring the long-term viability of the supply chain.

Sustainable smart cities based on the Internet of Things (IoT) technology provide promising prospects for improving quality of life. However, in order to facilitate the widespread implementation of IoT-based smart city solutions, there is a need to concern about data privacy and security, standardization, interoperability, scalability, and sustainability. Reducing the environmental effect of urban activities, optimizing the management of energy resources, and designing novel services and solutions for inhabitants are all examples of how the smart city concept is inextricably linked to sustainability. There is a need to assess challenges in smart sustainable cities based on IoT. This paper is intended to introduce a new neutrosophic framework for assessment challenges in smart, sustainable cities based on IoT for better-managing energy resources and decreasing the urban environment's ecological impact. The proposed framework used nine criteria and five alternatives. Also, the proposed framework applied the neutrosophic Weighted Product Method (WPM) to compute the weights of criteria and rank challenges. Moreover, the proposed framework integrated the single-valued neutrosophic set to deal with uncertain data. The results indicated that the proposed framework can handle uncertain data and give more effective results in assessing challenges in smart, sustainable cities based on IoT to better manage energy resources and decrease the urban environment's ecological impact.

A popular technique used in many industries to assist decision-makers in evaluating and selecting the best alternatives from a variety of accessible possibilities is multi-criteria decision-making (MCDM). This study proposed an MCDM methodology for selecting optimal charcoal from various options and criteria. Various criteria influence the selection problem, such as financial, safety, labour, power supply, production, and transportation. This study used the Combinative Distance Assessment (CODAS) method as an MCDM method to rank the alternatives and use the best one. This study used nine criteria and twenty alternatives. The requirements are divided into positive and negative criteria to compete for the positive and negative ideal solution using the CODAS method. The criteria weights are computed. The rank of alternatives is checked by using the sensitivity analysis. The results show the rank of other options is stable in different cases.

In today's global economy, the Healthcare supply chain (HCSC) must be aware of a market that is both complicated and ever-changing. The HCSC in Industry 4.0 aims to give patients the medicine they need as quickly and cheaply as possible. The HCSC can improve operations by automating routine tasks and reducing human error by using new technologies like the "Internet of Things" (IoT); so, there is a need to analyze the role of IoT and Industry 4.0 in the Healthcare Supply Chain. This paper aims to analyze the role of IoT and Industry 4.0 in HCSC. Also, this paper proposes a framework using a single-valued neutrosophic set with multi-criteria decision-making (MCDM) to overcome uncertainty and deal with the various criteria and features. This paper used the 18 criteria and nine suppliers as an application in the healthcare supply chain. The results concluded that the proposed framework could handle uncertain data and give more effective results in analyzing the role of IoT and Industry 4.0 in HCSC.

Supplier assessment plays a pivotal role in various sectors and is a key component of supply chain management (SCM); however, it combines complexities and challenges associated with uncertainty and dynamic requirements. Several studies have applied various methods to assess suppliers; however, their calculations are complex. Therefore, this work introduces a new hybrid method, depending on the full consistency method (FUCOM) with triangular fuzzy numbers (TrFNs) to consistently prioritize supplier assessment criteria, using the distance from the ideal assessment criterion to assess various suppliers, as well as to handle uncertainty. The proposed method is applied to a practical case study, and the results show that it prioritizes the assessment criteria, reflecting stakeholder preferences. Moreover, it enables decision makers to construct a set of decision matrices and take into account various viewpoints, and it uses simple, detailed steps with TOPSIS, thus avoiding confusion. Additionally, the results indicate that the use of the FUCOM-TrFNs provides consistent weights and a robust tool for imprecise problems, with fewer comparisons and less confusion during calculations. Ultimately, the findings provide valuable insights for assessing and selecting suitable suppliers with a more applicable method.

The deployment of solar and wind energy technologies has witnessed remarkable growth globally. Solar wind energy requires careful grid management and the development of energy storage technologies to ensure a stable and reliable electricity supply. The selection policy for solar and wind energy is crucial to promoting the efficient and effective deployment of renewable energy technologies. This study provides an overview of the critical considerations and criteria for the selection policy for solar and wind energy projects. It emphasizes the importance of site suitability, resource assessment, technological feasibility, economic viability, environmental impact, community engagement, and regulatory compliance. By implementing a robust selection policy, policymakers and stakeholders can ensure the successful implementation of solar and wind energy projects that contribute to a sustainable and low-carbon energy future. This study builds a framework with three stages. In the first stage, we make the decision matrix between criteria and alternatives by the experts and decision makers. In the second stage, we compute the criteria weights by the average method. In the final stage, we used the Magnitude of the Area for the Ranking of Alternatives (MARA) Method to rank the alternatives. We used 10 criteria, and 15 policies to select the policies in solar wind energy. The results concluded that the proposed method could provide a new method to rank alternatives and select the best policy in solar wind energy. Moreover, the proposed method can introduce simpler and more flexible methods.