Dispatching rule selection with Gaussian processes

DOI10.1007/S10100-013-0322-7zbMath1339.90336OpenAlexW2023692613MaRDI QIDQ301496

Jens Heger, Bernd-Scholz Reiter, Torsten Hildebrandt

Publication date: 30 June 2016

Published in: CEJOR. Central European Journal of Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10100-013-0322-7

zbMATH Keywords

Gaussian processesmachine learningdispatching rulesplanning and schedulingproduction management and logistics

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Deterministic scheduling theory in operations research (90B35) Approximation methods and heuristics in mathematical programming (90C59)

Uses Software

• WEKA
• SIMLIB

Cites Work

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• A survey of priority rule-based scheduling
• A comparative study of dispatching rules in dynamic flowshops and jobshops
• Bayesian learning for neural networks
• A review of machine learning in dynamic scheduling of flexible manufacturing systems
• A Survey of Scheduling Rules
• An intelligent controller for manufacturing cells
• A Lyapunov–Razumikhin approach for stability analysis of logistics networks with time-delays

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