Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction

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Publicado en:Mathematics vol. 13, no. 11 (2025), p. 1842
Autor principal: Kwon Gahyeon
Otros Autores: Shim Yeongeun, Cho Kyungwoon, Bahn Hyokyung
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MDPI AG
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100 1 |a Kwon Gahyeon  |u Department of Computer Engineering, Ewha University, Seoul 03760, Republic of Korea; sonaby2@ewhain.net (G.K.); shim161030@ewhain.net (Y.S.) 
245 1 |a Real-Time Task Scheduling and Resource Planning for IIoT-Based Flexible Manufacturing with Human–Machine Interaction 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The emergence of Flexible Manufacturing Systems (FMS) presents new challenges in Industrial IoT (IIoT) environments. Unlike traditional real-time systems, FMS must accommodate task set variability driven by human–machine interaction. As such variations can lead to abrupt resource overload or idleness, a dynamic scheduling mechanism is required. Although prior studies have explored dynamic scheduling, they often relax deadlines for lower-criticality tasks, which is not well suited to IIoT systems with strict deadline constraints. In this paper, instead of treating dynamic scheduling as a prediction problem, we model it as deterministic planning in response to explicit, observable user input. To this end, we precompute feasible resource plans for anticipated task set variations through offline optimization and switch to the appropriate plan at runtime. During this process, our approach jointly optimizes processor speeds, memory allocations, and edge/cloud offloading decisions, which are mutually interdependent. Simulation results show that the proposed framework achieves up to 73.1% energy savings compared to a baseline system, 100% deadline compliance for real-time production tasks, and low-latency responsiveness for user-interaction tasks. We anticipate that the proposed framework will contribute to the design of efficient, adaptive, and sustainable manufacturing systems. 
653 |a Schedules 
653 |a Scheduling 
653 |a Task scheduling 
653 |a Flexible manufacturing systems 
653 |a Microprocessors 
653 |a Planning 
653 |a Optimization 
653 |a Allocations 
653 |a Adaptation 
653 |a Resource scheduling 
653 |a Industrial applications 
653 |a Compliance 
653 |a Systems stability 
653 |a Real time 
653 |a Workloads 
653 |a Energy consumption 
653 |a Deadlines 
653 |a Resource management 
653 |a Run time (computers) 
700 1 |a Shim Yeongeun  |u Department of Computer Engineering, Ewha University, Seoul 03760, Republic of Korea; sonaby2@ewhain.net (G.K.); shim161030@ewhain.net (Y.S.) 
700 1 |a Cho Kyungwoon  |u Embedded Software Research Center, Ewha University, Seoul 03760, Republic of Korea; cezanne@ewha.ac.kr 
700 1 |a Bahn Hyokyung  |u Department of Computer Engineering, Ewha University, Seoul 03760, Republic of Korea; sonaby2@ewhain.net (G.K.); shim161030@ewhain.net (Y.S.) 
773 0 |t Mathematics  |g vol. 13, no. 11 (2025), p. 1842 
786 0 |d ProQuest  |t Engineering Database 
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856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3217738849/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3217738849/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch