Parallel Problem Solving from Nature - PPSN VIII
By Jingpeng Li, Uwe Aickelin. Lecture Notes in Computer Science, 2004
1,169 citations recorded by the source corpus.
Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person’s assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.
Subfields: Computer Networks and Communications, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/9d6bee490262aa9d55532ec4aa0ed5d5ba1fa3f2
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In Computer Networks and Communications
In Management Science and Operations Research
- Heidi A. Taboada
- David W. Coit
- Philippe De Bruecker
- Erik L. Demeulemeester
- Jorne Van den Bergh
- Jeroen Beliën
Citations in the corpus
- Personnel scheduling: A literature review (2013, background): “Examples can be found in hospitals, where schedules have to satisfy demand for a given number of nurses with different grades [189] or students have to be scheduled together with nurses or physicians [294].”
- Multi-objective scheduling problems: Determination of pruned Pareto sets (2008, background): “The “knee” (Das, 1999; Branke et al., 2004) region is formed by those solutions of the Pareto-optimal front where a small improvement in one objective would lead to a large deterioration in at least one other objective.”