Optimizing a multiple objective surgical case scheduling problem.

The scheduling of the operating theater on a daily base is a complicated task and is mainly based on the experience of the human planner. This, however, does not mean that this task can be seen as unimportant since the schedule of individual surgeries influences a medical department as a whole. Based on practical suggestions of the planner and on real-life constraints, we will formulate a multiple objective optimization model in order to facilitate this decision process. We will show that this optimization problem is NP-hard and hence hard to solve. Both exact and heuristic algorithms, based on integer programming and on implicit enumeration (branch-and-bound), will be introduced. These solution approaches will be thoroughly tested on a realistic test set using data of the surgical day-care center at the university hospital Gasthuisberg in Leuven (Belgium). Finally, results will be analyzed and conclusions will be formulated.Algorithms; Belgium; Branch-and-bound; Constraint; Data; Decision; Experience; Healthcare; Heuristic; Integer; Integer programming; Model; Optimization; Order; Processes; Real life; Scheduling; University;

Robust Optimization Framework to Operating Room Planning and Scheduling in Stochastic Environment

Arrangement of surgical activities can be classified as a three-level process that directly impacts the overall performance of a healthcare system. The goal of this dissertation is to study hierarchical planning and scheduling problems of operating room (OR) departments that arise in a publicly funded hospital. Uncertainty in surgery durations and patient arrivals, the existence of multiple resources and competing performance measures are among the important aspect of OR problems in practice. While planning can be viewed as the compromise of supply and demand within the strategic and tactical stages, scheduling is referred to the development of a detailed timetable that determines operational daily assignment of individual cases. Therefore, it is worthwhile to put effort in optimization of OR planning and surgical scheduling. We have considered several extensions of previous models and described several real-world applications. Firstly, we have developed a novel transformation framework for the robust optimization (RO) method to be used as a generalized approach to overcome the drawback of conventional RO approach owing to its difficulty in obtaining information regarding numerous control variable terms as well as added extra variables and constraints into the model in transforming deterministic models into the robust form. We have determined an optimal case mix planning for a given set of specialties for a single operating room department using the proposed standard RO framework. In this case-mix planning problem, demands for elective and emergency surgery are considered to be random variables realized over a set of probabilistic scenarios. A deterministic and a two-stage stochastic recourse programming model is also developed for the uncertain surgery case mix planning to demonstrate the applicability of the proposed RO models. The objective is to minimize the expected total loss incurred due to postponed and unmet demand as well as the underutilization costs. We have shown that the optimum solution can be found in polynomial time. Secondly, the tactical and operational level decision of OR block scheduling and advance scheduling problems are considered simultaneously to overcome the drawback of current literature in addressing these problems in isolation. We have focused on a hybrid master surgery scheduling (MSS) and surgical case assignment (SCA) problem under the assumption that both surgery durations and emergency arrivals follow probability distributions defined over a discrete set of scenarios. We have developed an integrated robust MSS and SCA model using the proposed standard transformation framework and determined the allocation of surgical specialties to the ORs as well as the assignment of surgeries within each specialty to the corresponding ORs in a coordinated way to minimize the costs associated with patients waiting time and hospital resource utilization. To demonstrate the usefulness and applicability of the two proposed models, a simulation study is carried utilizing data provided by Windsor Regional Hospital (WRH). The simulation results demonstrate that the two proposed models can mitigate the existing variability in parameter uncertainty. This provides a more reliable decision tool for the OR managers while limiting the negative impact of waiting time to the patients as well as welfare loss to the hospital

Integrated Planning in Hospitals: A Review

Efficient planning of scarce resources in hospitals is a challenging task for which a large variety of Operations Research and Management Science approaches have been developed since the 1950s. While efficient planning of single resources such as operating rooms, beds, or specific types of staff can already lead to enormous efficiency gains, integrated planning of several resources has been shown to hold even greater potential, and a large number of integrated planning approaches have been presented in the literature over the past decades. This paper provides the first literature review that focuses specifically on the Operations Research and Management Science literature related to integrated planning of different resources in hospitals. We collect the relevant literature and analyze it regarding different aspects such as uncertainty modeling and the use of real-life data. Several cross comparisons reveal interesting insights concerning, e.g., relations between the modeling and solution methods used and the practical implementation of the approaches developed. Moreover, we provide a high-level taxonomy for classifying different resource-focused integration approaches and point out gaps in the literature as well as promising directions for future research

Planning elective surgeries Analysis and comparison in a real case

This work focus on hospital surgical suite optimization, mainly in the efficient use of the operating rooms when planning elective surgeries. We studied a real case in a hospital in Lisbon. An integer linear programming model was developed to weekly schedule elective surgeries for the hospital surgical suite. The model was tested with real data collected from the hospital records. Non-optimal solutions obtained were improved with a simple and efficient improving heuristic. All solutions have actually improved through this process. These results were finally analyzed and compared with the ones the hospital really performed. The analysis shows that the solutions obtained by our approach improve the use of the surgical suite while respecting the conditions imposed by the hospital. The analysis also shows that the plans obtained by the proposed approach can be implemented

Scheduling the hospital-wide flow of elective patients

In this paper, we address the problem of planning the patient flow in hospitals subject to scarce medical resources with the objective of maximizing the contribution margin. We assume that we can classify a large enough percentage of elective patients according to their diagnosis-related group (DRG) and clinical pathway. The clinical pathway defines the procedures (such as different types of diagnostic activities and surgery) as well as the sequence in which they have to be applied to the patient. The decision is then on which day each procedure of each patient’s clinical pathway should be done, taking into account the sequence of procedures as well as scarce clinical resources, such that the contribution margin of all patients is maximized. We develop two mixed-integer programs (MIP) for this problem which are embedded in a static and a rolling horizon planning approach. Computational results on real-world data show that employing the MIPs leads to a significant improvement of the contribution margin compared to the contribution margin obtained by employing the planning approach currently practiced. Furthermore, we show that the time between admission and surgery is significantly reduced by applying our models

Operating theatre planning and scheduling in real-life settings.Problem analysis, models, and solution procedures

Falta palabras claveNowadays health care organizations experience an increasing pressure in order to provide their services at the lowest possible costs as a response to the combination of restrictive budgets, increasing waiting lists, and the aging of the population. In general, hospital resources are expensive and scarce, being the operating theatre the most critical and expensive resource. In most hospitals, the operating theatre is a complex system composed of operating rooms (ORs) together with their specialized equipment, preoperative and postoperative facilities and, finally, a diversity of human resources, including surgeons, anesthetists, nurses, etc. To handle such complexity, decisions related to operating theatre management are usually decomposed into three hierarchical decision levels, i.e.: strategic, tactical and operational. At the strategic level, hospital managers set the volume and the mix of surgeries that will be performed over a long-term horizon (typically, a year) to keep up acceptable size of waiting lists while achieving cost targets, thus making long-term decisions related to the dimensioning of surgical facilities (e.g. build new ORs, adding new recovery beds, etc.), the hiring of surgical staff (e.g. surgeons, nurses, etc.), the purchase of novel surgical devices, and the amount of operating theatre resources required by surgical specialties to perform their surgeries (OR time, number of beds, etc.). Once decisions at strategic level have been made, the operating theatre resources are allocated over a medium-term planning horizon (ranging from few weeks to 6 months) in the tactical level. Since the OR is both a bottleneck and the most expensive facility for most hospitals, surgical specialties are first assigned to OR days (i.e. a pair of an OR and a day) over the planning horizon, until the OR time allocated to each surgical specialty in the strategic level is reached. Then, the above assignment defines aggregate resource requirements for specialties, such as the demand of nurses, drugs, diagnostic procedures, laboratory tests, etc. Finally, the working shifts of human resources and their workload (e.g. the number of surgeries allocated to each surgeon) are defined over the medium-term planning horizon in order to achieve the volume of surgeries set by hospital managers. Finally, the surgical schedule is determined over a short-term planning horizon (ranging from few days to few weeks) at the operational level. The operational level is usually solved into two steps. The first step involves the determination of the date and the OR for a set of surgeries in the waiting list; while in the second step, a sequence of surgeries for each OR within each day in the planning horizon is obtained. Note that only a set of surgeries will be performed during the planning horizon due to capacity constraints (both facilities and human resources). The decomposition of the operational level into the two aforementioned steps intends to reduce the complexity of the resulting problem, although the quality of the so-obtained surgery schedule may be reduced due to the high interdependence among these two steps, being the integrated approach a popular topic of research. At the operational level, a feature greatly influencing the performance is the uncertainty in the surgical activities, as frequently large discrepancies between the scheduled duration and the real duration of the surgeries appear, together with the availability of the resources reserved for emergency arrivals. Despite the importance and the complexity of these hierarchical levels, decisions in practice are usually made according to the decision makers’ experience without considering the underlying optimization problems. Furthermore, the lack of usage of decision models and solution procedures causes the decision makers to consume long times on performing management tasks (e.g. determine the surgical schedule, react to unforeseen events, carry out what-if analyses, etc.), instead of healthcare tasks. The context discussed above stresses the need to provide healthcare decision makers with advanced operations research techniques (i.e. models and solution procedures) in order to improve the efficiency of the operating theatre resources and the quality of the healthcare services at the operational level. This Thesis is aimed at this goal