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The supply chains that make up the distribution networks are becoming more diverse as companies respond to global markets’ rigorous demands. Today, the immediate need for sustainable growth is to design, manufacture, and deliver the right product to experience drastic improvements in the right place, at the right price, and at the right time. As a massively distributed, integrated logistics infrastructure, a new “Physical Internet” (PI) model has been implemented to make the existing logistics structures more scalable and sustainable. This article discusses resource control in the PI’s modern supply chain and logistics systems while addressing the potential disruptions between the hubs during the transfer of goods. This article attempts to establish how companies will benefit from introducing the PI to optimise their strategic supply chain. This article used ATLAS.ti 9 automated tool to save, classify, and evaluate the data for this analysis to review the literature systematically. For PI, an increasing number of plans, blueprints, and requirements have been established. Still, minimal models are currently developed, explaining how the transformation from the strengthened logistics business models to the PI could occur. There is a lack of awareness of the necessary market structures that can include crucial players and allow the PI model to be embraced. Therefore, this research brings a new viewpoint on logistic operations through the PI idea, guarantees the present situation’s view, and presents a solution to the society–business–technology framework.

Designing and developing sustainable circular supply chain networks for electric vehicle (EV) lithium‐ion battery recycling and production requires complex environmental sustainability and economic viability assessment. EVs use a lot of data for battery management and delivering optimum performance, and the Internet of Things (IoT) plays a major role in managing this data. This study develops a bi‐objective mixed‐integer linear programming model for designing a sustainable circular supply chain to manage the manufacturing, remanufacturing, and distribution of EV lithium‐ion batteries under uncertainty using the IoT and big data. The proposed model simultaneously minimizes total costs and CO2 emissions and uses IoT to improve network performance and create a traceable and secure environment. A fuzzy multi‐objective method solves the bi‐objective optimization model under uncertainty, and a simulation algorithm examines the effectiveness of the proposed model through simulated problems. [ABSTRACT FROM AUTHOR]

The article discusses the importance of multidisciplinary collaboration in mitigating health crises that arise from wildlife-human interaction. It highlights the need for integrating data and knowledge from various fields, such as medicine, biology, epidemiology, and decision sciences, to develop a unified system for global ecosystem monitoring and management. The authors emphasize the significance of bridging the gap between academic research and practical implementation, particularly in the field of controlling infectious diseases. They propose a holistic approach that combines mathematical modeling, indigenous knowledge, decision sciences, and extended realities to prevent future pandemics and reduce the impact of wildlife diseases on human health. [Extracted from the article]

In this paper, an Intuitionistic Fuzzy TOPSIS model which is based on a score function is proposed for detecting the root cause of failure in an Offshore Boat engine, using groups of expert’s opinions. The study which has provided an alternative approach for failure mode identification and analysis in machines, addresses the machine component interaction failures which is a limitation in existing methods. The results from the study show that although early detection of failures in engines is quite difficult to identify due to the dependency of their systems from each other. However, with the Intuitionistic Fuzzy TOPSIS model which is based on an improved score function such faults/failures are easily detected using expert’s based opinions.

Manufacturing relies heavily on the physical presence of human resources, and disease outbreaks that force separation, such as: COVID-19, harm output. As a solution to the problem, this paper proposes a new Hybrid Multi-criteria Decision-making (H-MCDM) model based on the Intuitionistic Fuzzy Weighted Geometric (IFWG) operator, the Intuitionistic Fuzzy Hamacher Interactive Weighting Averaging (IFHIWA) operator, and the Intuitionistic Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (IF-TOPSIS) method for the evaluation, management, and prevention planning. In addition to the role that modeling played in solving the cross-infection problem, the results obtained from putting the model through its implementation show that the use of protective materials during manufacturing operations is the most suitable among the finite alternatives considered, when the safety of the workforce/workplace, cost of implementing risk-mitigating actions, virus transmission level, and ease of implementation are all taken into account. Thus, the proposed model has been validated through a feasibility test, so contributes to maintaining production and reducing cross-infections in workplaces while an epidemic is ongoing. [ABSTRACT FROM AUTHOR]