LEARNING DECISION RULES FOR SCHEDULING PROBLEMS: A CLASSIFIER HYBRID APPROACH
Elsevier eBooksPublished 1 January 1989
Michael R. Hilliard, Gunar E. Liepins, Gita Rangarajan, Mark Palmer
Citations15
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TL;DR
A series of experiments to learn general rules for simple job shop scheduling tasks suggest that the classifier system may work best as a component of a larger system.
Abstract
A series of experiments to learn general rules for simple job shop scheduling tasks suggest that the classifier system may work best as a component of a larger system. Preliminary results demonstrate the system's ability to learn binary decision rules as a component of a sorting routine.
Keywords
Computer ScienceEngineering
Medical Entomology and ZoologyMachine Scheduling Problems: Classification, complexity and computations
478 Citations1976Rinnooy Kan
The algorithm of McMahon and Florian 62, a branch-and-bound algorithm for solving the n|1|ri0|cmax problem, shows promise in solving the two-Machine and Three-Machine problems.
Machine learning applications to job shop scheduling
26 Citations1988Michael R. Hilliard, Gunar E. Liepins +1 more
This paper advocates augmenting expertly known heuristics for react ive scheduling with heuristic discovered through machine learning.