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Learning-based algorithms in scheduling
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DOI：10.1023/A:1008934615349.png)
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摘要
En 中文
The aim of the paper is to present a conception of intelligent learning-based algorithms for scheduling. A general knowledge based model of a vast class of discrete deterministic processes is given. The model is a basis for the method of the synthesis of intelligent, learning-based algorithms, that is described in the paper. The designing simulation experiments that use learning is also described. To illustrate the presented ideas, the scheduling algorithm for a special NP-hard problem is given. The significant feature of the problem is that the retooling time depends not only on a pair of jobs to be processed directly one after the other, but also on the subset of jobs already performed. The proof of the NP-hardness of the problem is also given in the paper.
Keyword:
learning algorithms
heuristics
manufacturing discrete process
processes
state graph
algebraic-logical model
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