A Self-Learning Based Dynamic Multi-Objective Evolutionary Algorithm for Resilient Scheduling Problems in Steelmaking Plants

Scheduling is one of the most important missions for plant-wide optimization in steelmaking manufacturing systems. In the context of dynamic scheduling, the decision-maker should simultaneously minimize economic objectives within the decision space and violation penalty out of the decision space. In...

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Vydané v:	IEEE transactions on automation science and engineering Ročník 20; číslo 2; s. 832 - 845
Hlavní autori:	Jiang, Sheng-Long, Liu, Qie, Bogle, I. David L., Zheng, Zhong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.04.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Casting Controllability Decision making Disturbances Dynamic scheduling Evolutionary algorithms evolutionary optimization Genetic algorithms Heuristic algorithms Job shop scheduling Linear programming Machine learning Manufacturing multi-objective Multiple objective analysis Optimization Production Resilience Scheduling self-learning Sensitivity analysis Steel making steelmaking
ISSN:	1545-5955, 1558-3783
On-line prístup:	Získať plný text
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