Bayesian heuristic approach to scheduling

Real life scheduling problems are solved by heuristics with parameters defined by experts, as usual. In this paper a new approach is proposed where the parameters of various heuristics and their random mixtures are optimized to reduce the average deviations from the global optimum. In many cases the average deviation is a stochastic and multi-modal function of heuristic parameters. Thus a stochastic global optimization is needed. The Bayesian heuristic approach is developed and applied for this optimization. That is main distinctive feature of this work. The approach is illustrated by flow-shop and school scheduling examples. Two versions of school scheduling models are developed for both traditional and profiled schools. The models are tested while designing schedules for some Lithuanian schools. Quality of traditional schedules is defined by the number of teacher ‘windows’. Schedules of profiled schools are evaluated by user defined penalty functions. That separates clearly subjective and objective data. This is the second specific feature of the proposed approach. The software is developed for the Internet environment and is used as a tool for research collaboration and distance graduate studies. The software is available at web-sites and can be run by standard net browsers supporting Java language. The care is taken that interested persons could easily test the results and apply the algorithms and software for their own problems.