Preemptive scheduling on unrelated machines with fractional precedence constraints

Aggarwal, Vaneet; Lan, Tian; Peddireddy, Dheeraj

doi:10.1016/j.jpdc.2021.07.010

Journal of Parallel and Distributed Computing

2021

DOI: 10.1016/j.jpdc.2021.07.010

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Preemptive scheduling on unrelated machines with fractional precedence constraints

Vaneet Aggarwal¹,

Tian Lan²,

Dheeraj Peddireddy³

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2023

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Cited by 1 publication

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Optimized Electrical Machine Operation Scheduling using Classification Learning

Dhyani,

Kumar,

Shelke

et al. 2023

Wseas Transactions on Power Systems

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Scheduling electrical machines based on consumer demands improves the efficiency of the purpose through flawless allocations. However, due to peak utilization and maximum run-time of the machines, the chances of schedule mismatch and overlapping are common in large production scales. In this paper, an Operation Scheduling process (OSP) using Classification Learning (CL) is proposed. The proposed process classifies operation schedules based on overlapping and mismatching intervals post-output completion. The classification is performed using interval stoppage and re-scheduling performed between successive completion intervals. This is required to improve the output success rate for simultaneous machine operations. Therefore the scheduling is improved regardless of distinct tasks allocated with better outcomes.

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Optimized Electrical Machine Operation Scheduling using Classification Learning

Dhyani,

Kumar,

Shelke

et al. 2023

Wseas Transactions on Power Systems

View full text Add to dashboard Cite

show abstract

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Preemptive scheduling on unrelated machines with fractional precedence constraints

Cited by 1 publication

References 15 publications

Optimized Electrical Machine Operation Scheduling using Classification Learning

Optimized Electrical Machine Operation Scheduling using Classification Learning

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