Abstract
Smart factory also known as smart manufacturing is an emerging field with the revolution of industry 4.0. With the help of all these concepts, the smart factory integrates the manufacturing assets and represents industrial networks. In this paper, we focus on integrated solutions for smart factory concerns; by proposing an efficient task management mechanism based on an efficient and resource-aware scheduling scheme named as ACM-FEF. The scheduling algorithm used for the efficient task management is hybrid of the two scheduling approaches as agent cooperation mechanism (ACM) and fair emergency first (FEF) scheduling scheme. ACM is a decentralized scheduling approach which focuses on the production maximization goals per machine, and also pays attention to the production goals of all the machine networks involved in the smart factory. FEF scheduling scheme focuses on minimizing the tasks starvation rate and maximizing the machine utilization by efficiently using the machine slots. The proposed hybrid mechanism aims to efficiently plan tasks execution, maximize machines’ resource utilization, maximize productivity, minimize production delays, efficiently handle exceptions and efficiently control smart factory actuators.
Highlights
Technology has vastly changed with the changes witnessed by the industrial revolutions
Efficient real-time tasks scheduling based on analytics and optimized techniques is vital for smart factory systems
We have proposed an integrated solution for efficient task management, in smart factory, based on a hybrid scheduling scheme
Summary
Technology has vastly changed with the changes witnessed by the industrial revolutions. The digital revolution for smart factories started after 1970s and continues till today, with rapid increase in automation and smart control of industry manufacturing by integrating first IT (Information Technology) and IoT based technologies, models, frameworks and solutions. The smart factory solution requires a real-time scheduling approach for ordering the tasks and jobs arriving at the machines. An optimal scheduling mechanism based on dynamic model, considering both machine structure and job assignments, is proposed as a solution to mitigate challenges faced by short term supply chain scheduling [22]. Reinforcement learning based optimal scheduling mechanism is proposed for real-time scheduling in smart factory [24]. A smart scheduling solution for smart factory should take into consideration all the factors of machine load balancing, optimizing machine goals, adaptive/dynamic scheduling, distributed scheduling, minimizing number of starving tasks, and learning based scheduling. Time budget is the difference between latest finish time and the finish time
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