Author:

Keywords:

ITEC, Science & Technology, Technology, Computer Science, Artificial Intelligence, Computer Science, Theory & Methods, Computer Science, Hyper-heuristic, Health care, Benchmark instances, PATIENT, OPTIMIZATION, iMinds, 0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0802 Computation Theory and Mathematics, Operations Research, 4602 Artificial intelligence, 4901 Applied mathematics

Abstract:

We present one general high-level hyperheuristic approach for addressing two timetabling problems in the health care domain: the patient admission scheduling problem and the nurse rostering problem. The complex combinatorial problem of patient admission scheduling has only recently been introduced to the research community. In addition to the instance that was introduced on this occasion, we present a new set of benchmark instances. Nurse rostering, on the other hand, is a well studied operations research problem in health care. Over the last years, a number of problem definitions and their corresponding benchmark instances have been introduced. Recently, a new nurse rostering problem description and datasets were introduced during the first Nurse Rostering Competition. In the present paper, we focus on this nurse rostering problem description. The main contribution of the paper constitutes the introduction of a general hyperheuristic approach, which is suitable for addressing two rather different timetabling problems in health care. It is applicable without much effort, provided a set of low-level heuristics is available for each problem. We consider the instances of both health care problems for testing the general applicability of the hyperheuristic approach. Also, improvements to the previous best results for the patient admission scheduling problem are presented. Solutions to the new nurse rostering instances are presented and compared with results obtained by an integer linear programming approach.