Uniform Parallel-Machine Scheduling for Minimizing Total Resource Consumption With a Bounded Makespan

Lin, Shih-Wei; Ying, Kuo‐Ching

doi:10.1109/access.2017.2735538

Cited by 8 publications

(2 citation statements)

References 29 publications

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“…Ref. [7] examined a uniform parallel-machine scheduling problem with the objective of minimizing the total resource consumption subject to a bounded makespan. They developed a metaheuristic and showed that it could outperform the particle swarm optimization heuristic and approximate the theoretical lower bound.…”

Section: Identical Parallel Machinesmentioning

confidence: 99%

“…If X ijkt = 0, which means that job k cannot be processed immediately after job j on production line i, this constraint becomes a redundant constraint. Equation (7) ensures that the completion time of each production line (F it ) is greater than or equal to the completion time of any job in that production line (C ijt ). F it will be used to calculate the objective value later.…”

Section: Decision Variablesmentioning

confidence: 99%

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A Hybrid Metaheuristic for the Unrelated Parallel Machine Scheduling Problem

Lin

Huang

2021

Mathematics

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The unrelated parallel machine scheduling problem aims to assign jobs to independent machines with sequence-dependent setup times so that the makespan is minimized. When many practical considerations are introduced, solving the resulting problem is challenging, especially when problems of realistic sizes are of interest. In this study, in addition to the conventional objective of minimizing the makespan, we further consider the burn-in (B/I) procedure that is required in practice; we need to ensure that the scheduling results satisfy the B/I ratio constrained by the equipment. To solve the resulting complicated problem, we propose a population-based simulated annealing algorithm embedded with a variable neighborhood descent technique. Empirical results show that the proposed solution strategy outperforms a commonly used commercial optimization package; it can obtain schedules that are better than the schedules used in practice, and it does so in a more efficient manner.

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Section: Identical Parallel Machinesmentioning

confidence: 99%

Section: Decision Variablesmentioning

confidence: 99%