University class scheduling problem is one of the most important and complex issues in the academic field. This problem is recognized as one of the NP-HARD issues due to its various limitations. On the contrary, genetic algorithms are commonly used to solve NP-HARD problems, which is one of the decision-making problems and is basically one of the most fundamental classes of complexity. The university course planning includes severe constraints such as classroom, classroom curriculum, and faculty. At the same time, some soft constraints should be considered, such as student and faculty preferences and favorite class time. In this research, as a novel contribution, an integer model for scheduling university classes is presented. In this model, the preferences of professors and students are in accordance with the satisfaction values obtained through questionnaires. Moreover, a genetic algorithm has been developed to solve the model. The results show that the classroom timeline by this algorithm goes well during each run. Moreover, considering an exploratory search for the genetic algorithm can greatly improve the performance of this algorithm.
Today, due to the increase in people’s awareness of environmental issues and the strict policies of governments, the competitiveness of companies depends on considering environmental issues at all levels of the supply chain. However, the implementation of green supply chain management strategies has lots of different risks. The main contribution of this research is to evaluate and rank the companies in the tire industry with an emphasis on the environmental risks of the sustainable supply chain using the hybrid best-worst method (BWM) and fuzzy VIKOR (FVIKOR). First, data analysis was implemented by applying the BWM technique, which has higher reliability than other similar techniques. Next, the importance of the indicators involved in the risk of the green supply chain, including operational, supply, product return, financial, demand, organizational, and government, was calculated. Finally, according to the calculated weights for each criterion, five active companies in the tire industry were ranked using the FVIKOR technique. The results of prioritizing criteria and subcriteria showed that “financial risks” are the most important indicator among the indicators involved in green supply chain risk. Among the subcriteria, “rates related to inflation and currency” from the cluster of financial risks were recognized as the most important subcriteria. Moreover, the results of the ranking of five companies in the tire industry indicated that Dana Company is in the best situation in terms of green supply chain risks. Finally, a series of practical suggestions for managers and a series of scientific suggestions for future research have been presented.
The Internet of Things (IoT) and blockchain are new concepts in the world of technology and communication, and communication in them is provided for any creature (human, objects), the ability to send data through communication networks, whether the Internet or intranet. IoT is known as one of the most important axes of future technology and has received considerable attention from all industries. Today, a large number of different parts of the Internet of Things are designed only for specific businesses, which indicates the acceptance of a large number of organizations to use this equipment, and of course, various technologies are also used in this field, including artificial intelligence and cloud computing. The purpose of this research is to investigate the use of Internet of Things in business and its business models. There are also frameworks for the easier development of business models, which this article is the framework of business models in electronic commerce for the program. Internet of Things and Blockchain according to the research/interview literature. These frameworks can be used by developers as a starting point for creating a business model for IoT and blockchain applications in developing countries.
The connection of smart devices using the Internet has dramatically changed the way people live, and this concept has also been extended to the industrial sector. This practice not only provides more stable, faster, and safer communications but also makes it possible to realize the concept of the smart factory in the fourth industrial revolution. The Internet of Things uses a unique Internet Protocol to identify, control, and transmit data to individuals as well as databases. Data is collected through the Internet of Things, stored in cloud storage, and managed and calculated through analytical tools. Internet of Things security is a field of technology that focuses on protecting connected devices and networks in the Internet of Things (IoT). Ensuring the safety of networks with connected IoT devices is critical. Security in the Internet of Things includes a wide range of techniques, strategies, protocols, and measures aimed at mitigating the ever-increasing vulnerabilities of the Internet of Things in modern businesses. The simultaneous connection of objects also brings privacy concerns. For this reason, in this research, an effort has been made to examine and analyze the most important privacy requirements in the Internet of Things in digital businesses in Industry 4.0. In this regard, by using experts' opinions and literature review, privacy requirements were extracted and evaluated using fuzzy non-linear decision-making methodology. The results showed that acquired and intrinsic information has the highest importance.
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