Please cite this article as: Javid Ghahremani-Nahr, Ramez Kian, Ehsan Sabet, A robust fuzzy mathematical programming model for the closed-loop supply chain network design and a whale optimization solution algorithm,

In today’s competitive world, supply chain management is one of the fundamental issues facing businesses that affects all an organization’s activities to produce products and provide services needed by customers. The technological revolution in supply chain logistics is experiencing a significant wave of new innovations and challenges. Despite the current fast digital technologies, customers expect the ordering and delivery process to be faster, and as a result, this has made it easier and more efficient for organizations looking to implement new technologies. “Artificial Intelligence of Things (AIoT)”, which means using the Internet of Things to perform intelligent tasks with the help of artificial intelligence integration, is one of these expected innovations that can turn a complex supply chain into an integrated process. AIoT innovations such as data sensors and RFID (radio detection technology), with the power of artificial intelligence analysis, provide information to implement features such as tracking and instant alerts to improve decision making. Such data can become vital information to help improve operations and tasks. However, the same evolving technology with the presence of the Internet and the huge amount of data can pose many challenges for the supply chain and the factors involved. In this study, by conducting a literature review and interviewing experts active in FMCG industries as an available case study, the most important challenges facing the AIoT-powered supply chain were extracted. By examining these challenges using nonlinear quantitative analysis, the importance of these challenges was examined and their causal relationships were identified. The results showed that cybersecurity and a lack of proper infrastructure are the most important challenges facing the AIoT-based supply chain.

The use of advanced computer technologies has dramatically changed marketing. Concepts such as smart, sustainable, and green marketing have emerged in the last 20 years. One of these new technologies is the Internet of Things (IoT), which has led to the development of the activities and performances of industries in various dimensions. For the various objects, such as people, processes, and data, involved in marketing activities, the Internet of Everything (IoE) as an evolved IoT is a possible future scenario. Some sectors pretend to be the first to implement this, and the more they rely on dynamic, unstable customer needs, the better a solution the IoE is for them. Therefore, this paper presents a clear vision of smart, sustainable marketing based on the IoE in one of the fast-moving consumer goods (FMCG) industries, the dairy industry. Key factors are identified to help readers understand this concept better. The expert interview makes it possible to draw a picture of the factors that have helped successfully implement the IoE in the dairy sector.

In this paper, a sustainable closed-loop supply chain problem is modelled in conditions of uncertainty. Due to the COVID-19 pandemic situation, the designed supply chain network seeks to deliver medical equipment to hospitals on time within a defined time window to prevent overcrowding and virus transmission. In order to achieve a suitable model for designing a sustainable closed-loop supply chain network, important decisions such as locating potential facilities, optimal flow allocation, and vehicle routing have been made to prevent the congestion of vehicles and transmission of the COVID-19 virus. Since the amount of demand in hospitals for medical equipment is unknown, the fuzzy programming method is used to control uncertain demand, and to achieve an efficient solution to the decision-making problem, the neutrosophic fuzzy method is used. The results show that the designed model and the selected solution method (the neutrosophic fuzzy method) have led to a reduction in vehicle traffic by meeting the uncertain demand of hospitals in different time windows. In this way, both the chain network costs have been reduced and medical equipment has been transferred to hospitals with social distancing.

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In this paper, a blood supply chain network (BSCN) is designed to reduce the total cost of the supply chain network under demand and transportation costs. The network levels considered for modeling include blood donation clusters, permanent and temporary blood transfusion centers, major laboratory centers and blood supply points. Other goals included determining the optimal number and location of potential facilities, optimal allocation of the flow of goods between the selected facilities and determining the most suitable transport route to distribute the goods to customer areas in uncertainty conditions. This study addresses the issue of blood prishability from blood sampling to distribution to customer demand areas. Given that the model was NP-hard, the MFGO algorithm were used to solve the model with a priority-based solution. The results of the design of the experiments showed the high efficiency of the MFGO algorithm in comparison with the PSO algorithm in finding efficient solutions. Also, the mean of the objective function in robust approach is more than the one in the deterministic approach, while the standard deviation of the first objective function in the robust approach is less than the one in the deterministic approach at all levels of the uncertainty factor.

The dramatic growth of the Internet of Things in various fields necessitates the use of artificial intelligence capabilities in the optimal use of data. By combining these technologies, it reduces cost, automation and productivity more dynamically. This hybrid technology is called artificial intelligence of things (AIoT). Methodology: Intelligent solutions in the supply chain, i.e. the use of the Internet of Things with the capability of artificial intelligence, has been able to make various industries great. Findings: Due to the colorful role of IoT technology in the sustainability of industrial systems, this paper provides a framework for the implementation of an AIoT-based green supply chain. This framework shows a clear path to understanding the impact of this hybrid supply chain technology. Originality/Value: In his paper, a framework for the implementation of an AIoT-based green supply chain is provided.

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