A multiobjective evolutionary algorithm for achieving energy efficiency in production environments integrated with multiple automated guided vehicles

Increasing energy shortages and environmental pollution have made energy efficiency an urgent concern in manufacturing plants. Most studies looking into sustainable production in general and energy-efficient production scheduling in particular, however, have not paid much attention to logistical fac...

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Vydané v:	Knowledge-based systems Ročník 243; s. 108315
Hlavní autori:	He, Lijun, Chiong, Raymond, Li, Wenfeng, Budhi, Gregorius Satia, Zhang, Yu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 11.05.2022 Elsevier Science Ltd
Predmet:	Algorithms Automated guided vehicles Automation Decoding Encoding Encoding-Decoding Energy consumption Energy efficiency Evolutionary algorithms Experiments Exploitation Genetic algorithms Idling Job shops Job-shop scheduling Learning strategies Machinery Manufacturing Multiobjective optimization Opposition-based learning Pollution Process engineering Production Production scheduling Sequences Shortages Transportation Work environment Energy efficiency Evolutionary algorithms Opposition-based learning Automated guided vehicles Multiobjective optimization Job-shop scheduling
ISSN:	0950-7051, 1872-7409
On-line prístup:	Získať plný text
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