Hybrid Programming-Based Scheduling Approach for Many Heterogeneous Computing Tasks With Asynchronous Generation in IIoT

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT e...

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Vydané v:	IEEE internet of things journal Ročník 12; číslo 15; s. 29384 - 29396
Hlavní autori:	Hu, Bingtao, Zhong, Ruirui, Wang, Yong, Wang, Tianyue, Feng, Yixiong, Zhou, MengChu, Tan, Jianrong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Cloud computing Completion time Computation Computational modeling Computing task Constraints Data exchange Dynamic scheduling Feasibility Heuristic algorithms Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Job shop scheduling Mathematical models Mathematical programming Optimal scheduling optimization Processor scheduling Scheduling
ISSN:	2327-4662, 2327-4662
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Abstract	Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.
AbstractList	Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.
Author	Zhou, MengChu Hu, Bingtao Wang, Tianyue Zhong, Ruirui Tan, Jianrong Feng, Yixiong Wang, Yong
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SubjectTerms	Algorithms Cloud computing Completion time Computation Computational modeling Computing task Constraints Data exchange Dynamic scheduling Feasibility Heuristic algorithms Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Job shop scheduling Mathematical models Mathematical programming Optimal scheduling optimization Processor scheduling Scheduling
Title	Hybrid Programming-Based Scheduling Approach for Many Heterogeneous Computing Tasks With Asynchronous Generation in IIoT
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