Dynamic patient admission scheduling with operating room constraints, flexible horizons, and patient delays

Abstract

We revisit and extend the patient admission scheduling problem, in order to make it suitable for practical applications. The main novelty is that we consider constraints on the utilisation of operating rooms for patients requiring a surgery. In addition, we propose a more elaborate model that includes a flexible planning horizon, a complex notion of patient delay, and new components of the objective function. We design a solution approach based on local search, which explores the search space using a composite neighbourhood. In addition, we develop an instance generator that uses realworld data and statistical distributions so as to synthesise realistic and challenging case studies, which are made available on the web along with our solutions and the validator. Finally, we perform an extensive experimental evaluation of our solution method including statistically principled parameter tuning and an analysis of some features of the model and their corresponding impact on the objective function.

Case mix classification and a benchmark set for surgery scheduling

“…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. The instances are generated based on randomly generated theoretical case mixes.…”

“…However, for healthcare scheduling problems only a few benchmark sets exist, and no benchmark sets have been developed for surgery scheduling problems. The patient admission scheduling set of Ceschia and Schaerf (2016) is the closest to a surgery scheduling benchmark set available. An effective benchmark set should satisfy four conditions: diversity, realism, size, and extensibility.…”

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

“…This results in an infeasible solution, violating either the requirement that a patient be admitted before his/her maximal admission date or the requirement that there is a fixed number of days in the planning horizon. To be comparable to Ceschia and Schaerf [12], these infeasibilities receive an extra penalty of 200 per occurrence to ensure that they are quickly remedied during the optimization process.…”

“…Vancroonenburg et al [10] present two ILPs for dynamic versions of the six smallest data sets given in Demeester et al [6], and consider the impact of emergency patients and patient length of stay estimates. More recently, Ceschia and Schaerf [12] have extended the work of Ceschia and Schaerf [11] to also consider the scheduling of operating theaters.…”

Organizing integrated processes in cancer care

“…A patient admission scheduling benchmark set [35] has been generated on the basis of a single real-life instance from Demeester et al [83]. Since only limited real life data was available, Ceschia and Schaerf [61] presented a benchmark set for patient admission scheduling, together with an instance generator, solution validator, and first solutions. The instances are generated based on randomly generated theoretical case mixes.…”