Discovering dispatching rules using data mining

DOI10.1007/s10951-005-4781-0zbMath1123.90031OpenAlexW2069641426MaRDI QIDQ880519

Sigurdur Ólafsson, Xiao-Nan Li

Publication date: 15 May 2007

Published in: Journal of Scheduling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10951-005-4781-0

Mathematics Subject Classification ID

Deterministic scheduling theory in operations research (90B35) Artificial intelligence (68T99)

Related Items (8)

Data mining-based dispatching system for solving the local pickup and delivery problem ⋮ Learning-augmented heuristics for scheduling parallel serial-batch processing machines ⋮ Operations research and data mining ⋮ Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: a state-of-the-art ⋮ Auction optimization using regression trees and linear models as integer programs ⋮ Synergies of operations research and data mining ⋮ Learning heuristics for basic block instruction scheduling ⋮ Composite Dispatching Rule Generation through Data Mining in a Simulated Job Shop

Uses Software

• C4.5

Cites Work

• A review of machine learning in dynamic scheduling of flexible manufacturing systems
• Dynamic scheduling system utilizing machine learning as a knowledge acquisition tool
• Indentifying attributes for knowledge-based development in dynamic scheduling environments
• Job shop scheduling with a genetic algorithm and machine learning
• Job-shop scheduling using neural networks
• A competitive neural network approach to multi-objective FMS scheduling
• Integration of inductive learning and neural networks for multi-objective FMS scheduling
• PlanMine: Prediction plan failures using sequence mining
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