The Art of Building Decision Trees
Journal of Medical Systems
Towards More Optimal Medical Diagnosing with Evolutionary Algorithms
Journal of Medical Systems
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
A knowledge-based scheduling system for Emergency Departments
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Editorial: AI planning and scheduling in the medical hospital environment
Artificial Intelligence in Medicine
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In medicine and health care there are a lot of situations when patients have to be scheduled on different devices and/or with different physicians or therapists. It may concern preventive examinations, laboratory tests or convalescent therapies, therefore we are always looking for an optimal schedule that would result in finishing all the activities scheduled as soon as possible, with the least patient waiting time and maximum device utilization. Since patient scheduling is a highly complex problem, it is impossible to make a qualitative schedule by hand or even with exact heuristic methods. Therefore we developed a powerful automated scheduling method for highly constrained situations based on genetic algorithms and machine learning. In this paper we present the method, together with the whole process of schedule generation, the important parameters to direct the evolution and how the algorithm is guaranteed to produce only feasible solutions, not breaking any of the required constraints. We applied the described method to a problem of scheduling patients with different therapy needs to a limited number of therapeutic devices, but the algorithm can be easily modified for use in similar situations. The results are quite encouraging and since all the solutions are feasible, the method can be easily incorporated into an interactive user interface, which can be of major importance when scheduling patients, and human resources in general, is considered.