One hyper-heuristic approach to two timetabling problems in health care

Abstract

We present one general high-level hyper-heuristic 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 lowlevel heuristics is available for each problem. We consider the instances of both health B. Bilgin · P. Demeester ( ) · M. Misir · W.B. Bilgin et al.care problems for testing the general applicability of the hyper-heuristic 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.

Case mix classification and a benchmark set for surgery scheduling

“…Although various benchmark sets exist for well-known combinatorial optimization problems, for healthcare planning and scheduling there only exist various benchmark sets for nurse scheduling (Brucker et al 2010;Musliu et al 2004;Vanhoucke and Maenhout 2009) and patient admission scheduling (Bilgin et al 2012). Typically, these benchmark sets, such as NSPLib (Vanhoucke and Maenhout 2009), are not real-world-based instances but are generated randomly.…”

“…In the first competition, three tracks were presented, based on the available running time of the algorithms, including mall, medium, and large sized instances to solve (Haspeslagh et al 2014). A patient admission scheduling benchmark set (Bilgin et al 2012) has been generated on the basis of a single reallife instance from Demeester et al (2010). Since only limited real-life data were available, Ceschia and Schaerf (2016) presented a benchmark set for patient admission scheduling, together with an instance generator, solution validator, and first solutions.…”

An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem

“…The authors proposed a hybrid tabu search algorithm to solve this problem. The same instances have subsequently been considered by Ceschia and Schaerf [7], Bilgin et al [8], and Range et al [9]. The first two contributions also consider local search based heuristics, while the last describes a column generation based heuristic approach combined with constraint aggregation.…”

A column generation approach for solving the patient admission scheduling problem

“…The remaining work in this area, Demeester et al [2010], Bilgin et al [2012], Ceschia and Schaerf [2011], all consider the same version of PAS. This definition of the PAS problem was originally presented in Demeester et al [2010].…”