285 research outputs found
Developing a multi-methodological approach to hospital operating theatre scheduling
Operating theatres and surgeons are among the most expensive resources in any hospital, so it is vital that they are used efficiently. Due to the complexity of the challenges involved in theatre scheduling we split the problem into levels and address the tactical and day-to-day scheduling problems.Cognitive mapping is used to identify the important factors to consider in theatre scheduling and their interactions. This allows development and testing of our understanding with hospital staff, ensuring that the aspects of theatre scheduling they consider important are included in the quantitative modelling.At the tactical level, our model assists hospitals in creating new theatre timetables, which take account of reducing the maximum number of beds required, surgeons’ preferences, surgeons’ availability, variations in types of theatre and their suitability for different types of surgery, limited equipment availability and varying the length of the cycle over which the timetable is repeated. The weightings given to each of these factors can be varied allowing exploration of possible timetables.At the day-to-day scheduling level we focus on the advanced booking of individual patients for surgery. Using simulation a range of algorithms for booking patients are explored, with the algorithms derived from a mixture of scheduling literature and ideas from hospital staff. The most significant result is that more efficient schedules can be achieved by delaying scheduling as close to the time of surgery as possible, however, this must be balanced with the need to give patients adequate warning to make arrangements to attend hospital for their surgery.The different stages of this project present different challenges and constraints, therefore requiring different methodologies. As a whole this thesis demonstrates that a range of methodologies can be applied to different stages of a problem to develop better solutions
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
Hospital resource planning : a case-based application for surgical services of a Colombian hospital
Para la toma de decisiones estratégicas se hace uso de la Planificación de recursos hospitalarios
(HRP, por sus siglas en inglés) la cual es importante para permitir una gestión eficiente de los
recursos, identificar aquellos que causan cuellos de botella, mejorar el flujo de pacientes, brindar
tratamiento oportuno y reducir los costos. Este proyecto propone y desarrolla un aplicativo que
implementa un modelo cuantitativo de HRP para el servicio quirúrgico de cirugÃas electivas en un
hospital colombiano mediante el uso de un algoritmo genético con escenarios de demanda
estocástica como método de solución. El aplicativo propuesto toma en cuenta el impacto en la
programación táctica y operativa de los recursos quirúrgicos para cirugÃas electivas, este impacto se
refleja en la interacción con una herramienta de programación.For strategic decision making, the implementation of Hospital Resource Planning (HRP) is important to allow efficient resource management, identify those resources which are causing bottlenecks, improve patient flow, and provide timely treatment and reduce costs. This project proposes and develops an application that implements a quantitative HRP model for the elective surgical service for a Colombian Hospital by using a Genetic Algorithm with stochastic demand scenarios as solution method. The application takes into account its impact on tactical and operative scheduling of surgical resources for elective surgeries, this impact is reflected in the interaction with a scheduling tool.Ingeniero (a) IndustrialPregrad
Robust optimisation of operating theatre schedules
Hospitals in the UK are increasingly having to cancel a large proportion of elective operations due to the unavailability of beds on hospital wards for post-operative recovery. The availability of post-operative beds is therefore critical to the scheduling of surgical procedures and the throughput of patients in a hospital. The focus of this research is to investigate, via data-driven modelling, systematic reasons for the unavailability of beds and to demonstrate how the Master Surgery Schedule (MSS) can be constructed using Operational Research techniques to minimise the number of cancellations of elective operations.
Statistical analysis of data provided by the University Hospital of Wales, Cardiff was performed, providing information on patient demand and length of stay distributions. A two-stage modelling process was developed to construct and simulate an MSS that minimises the number of cancellations. The first stage involves a novel set partitioning based optimisation model that incorporates operating room and bed constraints. The second stage simulates the resulting optimal schedule to provide measures on how well the schedule would perform if implemented. The results from this two-stage model provide insights into when best to schedule surgical specialties and how best the beds are distributed between wards.
Two optimisation under uncertainty techniques are then employed to incorporate the uncertainty associated with the bed requirements into the optimisation process. A robust optimisation (RO) approach that uses protection functions in each bed constraint is developed. Investigations into varying levels of protection are performed in order to gain insight into the so called `price of robustness'. Results show that MSSs that are constructed from protecting more of the uncertainty result in fewer cancellations and a smaller probability of requiring more beds than are available.
The deterministic optimisation model is then extended to become a scenario-based optimisation model in which more scenarios of bed requirement are incorporated into a single optimisation model. Results show that as more scenarios are included, a more robust schedule is generated and fewer cancellations are expected.
Results from the different approaches are compared to assess the benefits of using RO techniques. Future research directions following from this work are discussed, including the construction of the MSS based on sub-specialties and investigation of different working practices within the case study hospital
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