A new QoS-aware method for production scheduling in the industrial internet of things using elephant herding optimization algorithm
AuthorAvval, Danial Bakhshayeshi
Heris, Pouria Ouni
Navimipour, Nima Jafari
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The Internet of Things (IoT) is a network of physical items implanted with software, sensors, etc., to link and exchange data with other devices. These devices vary in complexity from common household items to sophisticated industrial instruments. It would be challenging to choose an appropriate IoT service based on the requirements of the vast pool of accessible services with similar capabilities, given the growth of IoT-based service providers in the market. A suitable selection may be made using quality-of-service (QoS) parameters that characterize a service. IoT has several benefits over traditional communication systems. Also, it is a component of a safe and smart city system known as the Industrial Internet of Things (IIoT) which is particularly useful in the industrial field. However, it suffers from various issues such as high costs, energy consumption, and long delays. The production scheduling problem is one of the main issues in IIoT, and it is an NP-hard problem regarding cost and energy efficiency. Therefore, a meta-heuristic algorithm based on the elephant herd optimization algorithm is proposed to minimize resource costs, conversion costs, and the cost of continuous development delays. By combining the clan updating factor, separating operator, and the proposed algorithm, we created an effective and efficient method to solve the issue of production scheduling. Many experiments are performed to determine the performance of industrial environments. The outcomes demonstrate that the suggested technique can optimize planning and achieve cost reduction, efficient energy consumption, and latency decrease.