Operating Room Scheduling by Using Hybrid Genetic Algorithm

Hospitals are among the most important institutions of today. For hospitals, efficient use of operating rooms is of great importance. Efficient use of operating rooms is a problem that needs to be solved. The operating room scheduling problem is a very complex problem with large number of constraints. This type of problem called as NP-Hard type problem. NP-Hard type problems do not consist of polynomial values. Therefore, the solution of these problems is very complex and difficult. Solutions consisting of polynomial values can be solved effectively with existing mathematical methods. However, more effective algorithms were needed to solve NP-hard type problems. As a result of the studies, many heuristic, meta-heuristic algorithms such as Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Taboo Search Algorithm have been developed to solve the complexity of NP-Hard problems. In this article, the operating room scheduling problem solved with a hybrid genetic algorithm. In this solution, it shows how the algorithm affects the solution area in the changes in the number of surgeons, operating rooms and operating room reservations, which are among the operating room parameters. In the developed software, C# programming language has been preferred in order to provide comfortable use of the end user

An approximate dynamic programming approach to the admission control of elective patients

In this paper, we propose an approximate dynamic programming (ADP) algorithm to solve a Markov decision process (MDP) formulation for the admission control of elective patients. To manage the elective patients from multiple specialties equitably and efficiently, we establish a waiting list and assign each patient a time-dependent dynamic priority score. Then, taking the random arrivals of patients into account, sequential decisions are made on a weekly basis. At the end of each week, we select the patients to be treated in the following week from the waiting list. By minimizing the cost function of the MDP over an infinite horizon, we seek to achieve the best trade-off between the patients' waiting times and the over-utilization of surgical resources. Considering the curses of dimensionality resulting from the large scale of realistically sized problems, we first analyze the structural properties of the MDP and propose an algorithm that facilitates the search for best actions. We then develop a novel reinforcement-learning-based ADP algorithm as the solution technique. Experimental results reveal that the proposed algorithms consume much less computation time in comparison with that required by conventional dynamic programming methods. Additionally, the algorithms are shown to be capable of computing high-quality near-optimal policies for realistically sized problems

Operating room and surgical team members scheduling: A comprehensive review

Operating rooms (OR) are one of the most expensive parts of a hospital with complex processes, and the efficient use of resources is of utmost importance. Therefore, proper management and operation of operating rooms are extremely crucial. OR scheduling ensures that the surgeries are performed at the proper time, patients are treated effectively and safely, resources are used effectively, and staff is increased in work efficiency. Furthermore, accurately scheduled surgeries are safer for patients' healing processes. This is dependent on factors such as the availability of qualified personnel at the appropriate time, the readiness of surgical equipment, and the provision of proper sterilization and hygienic conditions. Surgical team scheduling ensures that surgeries begin on time, are completed effectively, and patients are treated safely. It is also critical to reduce employee fatigue and balance the workload. As a result, integrating surgical teams into operating room scheduling problems provides significant benefits. Accordingly, 29 research articles focusing on the problem of OR scheduling, within the scope of constraints on surgical team members, scheduling strategies, uncertainties, and solution methods, are thoroughly reviewed in this study

Modelo matemático para alocação de especialidades cirúrgicas com prioridades hierarquizadas por meio do Analitycal Hierarchy Process (AHP)

Orientador: Prof. José Eduardo Pécora Junior, Ph.D.Dissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Produção. Defesa : Curitiba, 28/02/2019Inclui referências: p.63-67Área de concentração: Pesquisa OperacionalResumo: O agendamento de cirurgias eletivas às salas cirúrgicas é um processo que tem como objetivo determinar os horários de início de cada cirurgia e qual especialidade deve ser alocada à sala, considerando todas as restrições de recursos disponíveis para garantir fluxo nas atividades dos centros cirúrgicos. No entanto, este processo é passível de imprevistos os quais acabam gerando cancelamentos de horários já programados. Diante disso, surge a seguinte pergunta: qual especialidade alocar para este horário? Este trabalho tem como objetivo propor um modelo de priorização de especialidades cirúrgicas por meio do método Analytic Hierarchy Process (AHP). Ainda, utilizar os pesos atribuídos a cada especialidade em um modelo matemático para apoiar a decisão de escolha de qual especialidade cirúrgica alocar para uma ou mais salas vagas. Uma revisão sistemática de literatura foi apresentada para buscar respostas de como determinar os critérios de priorização das especialidades disponíveis e sobre qual abordagem matemática utilizar para o modelo. Com isso, é proposto o método AHP combinado com um modelo matemático de programação linear inteira para apoiar a decisão de alocação da especialidade à sala dado que um ou mais horários foram disponibilizados por cancelamento. Por fim, uma aplicação do modelo proposto é realizada nas dependências do centro cirúrgico do Hospital de Clínicas da Universidade Federal do Paraná. Palavras-chave: Centro Cirúrgico, Critérios de Seleção de Especialidades Cirúrgicas, Modelo Matemático, AHP.Abstract: The scheduling of elective surgeries to surgical rooms is a process that aims to determine the opening times of each surgery and which specialty should be allocated to the room, considering all the restrictions of available resources to guarantee flow in the surgical centers activities. However, this process is subject to unforeseen events which end up generating cancellations of schedules already scheduled. Given this, the question arises: which specialty to allocate for this time? This work aims to propose a prioritization model of surgical specialties through the Analytic Hierarchy Process (AHP) method. Also, use the weights assigned to each specialty in a mathematical model to support the decision of choosing which surgical specialty to allocate to one or more vacant rooms. A systematic literature review was presented to seek answers on how to determine the prioritization criteria of the specialties available and on which mathematical approach to use for the model. Thus, the AHP method combined with a mathematical model of integer linear programming is proposed to support the decision to allocate the specialty to the room given that one or more schedules were made available by cancellation. Finally, an application of the proposed model is performed in the surgical center of the Hospital de Clínicas of the Federal University of Paraná. Key-words: Surgerical Center, Selection Criteria for Surgical Specialties, Mathematical Model, Optimizatio

Secuenciación integrada de pacientes en quirófanos y unidades médicas

Los quirófanos constituyen el recurso más crítico del sistema sanitario. Suponen la mayor partida de costes del hospital a la vez que soportan una gran presión derivada de las largas listas de espera de pacientes. La programación de quirófanos adquiere una relevancia sustancial, siendo objeto de investigación de numerosos autores. Sin embargo, existe un vacío en la literatura en lo que respecta a la consideración de distintas unidades médicas dentro del sistema quirúrgico. Este trabajo responde a un objetivo dual, obteniendo una secuenciación de los pacientes según la prioridad clínica de su cirugía (1) y una distribución de unidades médicas que consiga optimizar el nivel de servicio ofrecido (2). Este estudio resuelve un problema de optimización-simulación. La fase de optimización concierne un modelo de programación lineal entera (ILP) y se refiere a un nivel de decisión operativo que arroja como resultado la elaboración de un programa quirúrgico. Este nivel de planificación obedece a las decisiones tácticas derivadas de la asignación de unidades a los quirófanos, a resolver mediante el estudio de simulación integrado. El objetivo del estudio reside, en última instancia, en identificar la mejor asignación de unidades en los distintos quirófanos de forma que se maximice el nivel de servicio ofrecido a los pacientes. El consiguiente programa quirúrgico constituye un plan hipotético al considerar una de las infinitas posibilidades del escenario hospitalario, debido a su naturaleza estocástica. Es mediante la generación de un extenso conjunto de instancias (test bed), como se consigue hallar la combinación de unidad-quirófano que mejor funciona de forma genérica. La experimentación realizada al respecto pone de manifiesto los límites computacionales a los que se enfrenta el algoritmo. El modelo propuesto es aplicado a un entorno sanitario simulado, que replica las características de una unidad quirúrgica de un hospital del Sistema Sanitario Público Andaluz (España), evidenciando una mejora significativa en el número de cirugías realizadas, así como en la utilización de los recursos quirúrgicos.Operating rooms are the most critical resource in the healthcare system. They represent the largest cost item of the hospital meanwhile they deal with long waiting lists of patients. O perating room scheduling has been the subject of research by numerous authors. However, there is a gap in the literature regarding the consideration of different medical units within the surgical system. This work responds to a dual objective a sequencing of patients according to the clinical priority of their surgery (1) and a distribution of medical units that manages to optimize the level of service offered (2). This study solves an optimization simulation problem. The optimization phase concerns an integer linear programming (ILP) model and refers to an operational decision level resulting in the elaboration of a surgical program. This level of planning is due to the tactical decisions resulting from the allocation of units to the operating rooms, to be solved by the integrated simulation study. The objective of the study is ultimately to identify the best allocation of units in the various operating rooms so as to maximize the level of service offered to patients. The ensuing surgical program constitutes a hypothetical plan by considering one of the infinite possibilities of the hospital scenario, due to its stochastic nature. It is through the generation of an extensive set of instances ( test bed ), how to find the combination of operating room unit that works best in a generic way. The experimentation carried out in this respect reveals the computational limits faced by the algorithm. The proposed model is applied to a simulated healthcare environment, which replicates the characteristics of a surgical unit in the Public Sanitary System of Andalusia (Spain) Spain), showing a significant improvement in the number of surgeries performed, as well as enhancing resource utilization.Universidad de Sevilla. Máster en Organización Industrial y Gestión de Empresa

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

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