A data-driven methodology for the automated configuration of online algorithms

With the goal of devising algorithms for decision support in operational tasks, we introduce a new methodology for the automated configuration of algorithms for combinatorial online optimization problems. The procedure draws upon available instance data and is capable of recognizing data patterns wh...

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Veröffentlicht in:	Decision Support Systems Jg. 137; S. 113343
Hauptverfasser:	Dunke, Fabian, Nickel, Stefan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier B.V 01.10.2020 Elsevier Sequoia S.A
Schlagworte:	Algorithms Automated algorithm configuration Automated decision making Automation Combinatorial analysis Configurations Data-driven optimization Decision making Decision support systems Lean decision making Lot sizing Methodology Online optimization Optimization Pattern recognition Simulated annealing Online optimization Lean decision making Automated decision making Automated algorithm configuration Simulated annealing Data-driven optimization
ISSN:	0167-9236, 1873-5797
Online-Zugang:	Volltext
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