Adaptive scheduling algorithm based on mixed graph model

What is it about?

This paper deals with an adaptive approach for scheduling problems, The main idea is to produce, for a class of similar problems, a special heuristic rule which is successful for problems of this class. It is realised by 'tuning' the parameters of the algorithm while 'learning' the peculiarities of considering a class of problems. Once trained on the sample problems (in the authors' experiments a well-known test problem with 10 jobs and 10 machines has been considered), the adaptive algorithm solves 'close' problems better than a branch and bound algorithm with a time limit (both in running time and in accuracy of the constructed schedule) and better than the 25 heuristics (in accuracy) used in the learning stage.

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