Multitask Multiobjective Genetic Programming for Automated Scheduling Heuristic Learning in Dynamic Flexible Job-Shop Scheduling

Evolutionary multitask multiobjective learning has been widely used for handling more than one multiobjective task simultaneously. However, it is rarely used in dynamic combinatorial optimization problems, which have valuable practical applications such as dynamic flexible job-shop scheduling (DFJSS...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IEEE transactions on cybernetics Jg. 53; H. 7; S. 4473 - 4486
Hauptverfasser:	Zhang, Fangfang, Mei, Yi, Nguyen, Su, Zhang, Mengjie
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.07.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Business competition Combinatorial analysis Dynamic job-shop scheduling Dynamic scheduling Genetic algorithms genetic programming (GP) Heuristic Heuristic algorithms Heuristic job shop scheduling hyperheuristics Job shop scheduling Knowledge sharing Learning multiobjective Multiple objective analysis multitask learning Optimization Scheduling Sociology Solution space Statistics Strategy Task analysis Task scheduling
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!