Numerous benchmark sets exist for combinatorial optimization problems. However, in healthcare scheduling, only a few benchmark sets are known, mainly focused on nurse rostering. One of the most studied topics in the healthcare scheduling literature is surgery scheduling, for which there is no widely used benchmark set. An effective benchmark set should be diverse, reflect the real world, contain large instances, and be extendable. This paper proposes a benchmark set for surgery scheduling algorithms, which satisfies these four criteria. Surgery scheduling instances are characterized by an underlying case mix, which describes the volume and properties of the surgery types. Given a case mix, unlimited random instances can be generated. A complete surgery scheduling benchmark set should encompass the diversity of prevalent case mixes. We therefore propose a case mix classification scheme, which we use to typify both real-life and theoretical case mixes that span the breadth of possible case mix types. Our full benchmark set contains 20,880 instances, with a small benchmark subset of 146 instances. The instances are generated based on real-life case mixes (11 surgical specialties), as well as theoretical instances. The instances were generated using a novel instance generation procedure, which introduces the concept of "instance proximity" to measure the similarity between two instances, and which uses this concept to generate sets of instances that are as diverse as possible.

This paper proposes an iterative simulation optimisation approach to maximise the number of in-person consultations in the blueprint schedule of a clinic facing same-day multiappointment patient trajectories and restrictions on the number of patients simultaneously allowed in the waiting area, taking into account the combined effects of early arrival times (patients arriving early from home), bridging times (minimum time required between appointments) and waiting times (due to randomness in patient arrivals and provider punctuality). Our approach combines an Integer Linear Program (ILP) that maximises the number of in-person consultations considering the effect of average early arrival and bridging times and a Monte Carlo simulation (MCS) model to include the effect of waiting times due to randomness. We iteratively adapt our parameters in the ILP until the MCS model returns a 95% confidence interval of the number of patients in the waiting area that does not exceed its capacity. Our results reveal the impact of early arrival, bridging and waiting times on the number of in-person appointments that may be included in a blueprint schedule. Our results further show that careful design of the blueprint schedule allows our case study clinics to organise a vast majority of their appointments in-person.

This handbook provides our take on optimization of logistics processes in healthcare and on the gap that exists between theory and practice. We will bridge that gap as all theoretical results presented in this book have actually been implemented in the healthcare domain. We are driven by a desire to improve the healthcare system, by effectively making an impact with Operations Research (OR). We discuss specific projects that have addressed major challenges for healthcare Operations Research. We present our solution approaches, our approaches to implement the results in practice, and the impact on healthcare organizations. In addition, we discuss the problems we encountered when implementing the results in practice and how we addressed them. In this introductory chapter, we discuss the ecosystem of our research center CHOIR (Center for Healthcare Operations Improvement & Research) and demonstrate how we have an impact on healthcare logistics.

Patient no-shows and cancellations are a significant problem to healthcare clinics, as they compromise a clinic's efficiency. Therefore, it is important to account for both no-shows and cancellations into the design of appointment systems. To provide additional empirical evidence on no-show and cancellation behaviour, we assess outpatient clinic data from two healthcare providers in the USA and EU: no-show and cancellation rates increase with the scheduling interval, which is the number of days from the appointment creation to the date the appointment is scheduled for. We show the temporal cancellation behaviour for multiple scheduling intervals is bimodally distributed. To improve the efficiency of clinics at a tactical level of control, we determine the optimal booking horizon such that the impact of no-shows and cancellations through high scheduling intervals is minimised, against a cost of rejecting patients. Where the majority of the literature only includes a fixed no-show rate, we include both a cancellation rate and a time-dependent no-show rate. We propose an analytical queuing model with balking and reneging, to determine the optimal booking horizon. Simulation experiments show that the assumptions of this model are viable. Computational results demonstrate general applicability of our model by case studies of two hospitals.

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