Dienstplanerstellung in Krankenhaeusern mittels genetischer Algorithmen

TL;DR

This paper explores how incorporating problem-specific knowledge into genetic algorithms can improve the efficiency of creating hospital staff schedules, emphasizing the importance of information selection and integration methods.

Contribution

It demonstrates that tailored problem-specific knowledge significantly enhances genetic algorithm performance in hospital scheduling problems.

Findings

Problem-specific knowledge improves genetic algorithm efficiency

Choice and integration method of information are crucial

Enhanced scheduling solutions for hospitals

Abstract

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems. It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.