The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.

[pt] As doenças vasculares são enfermidades graves e seus tratamentos são complexos e necessitam de procedimentos cirúrgicos. Para a realização desses procedimentos, são necessários equipamentos, equipes qualificadas e unidade de terapia intensiva (UTI) equipada para o pós-operatório. Hospitais de ensino devem atender à legislação vigente que preconiza um número mínimo de cirurgias para aprovação do residente no programa de formação. Assim, propõe-se, via otimização, encontrar soluções eficientes para o planejamento e programação de cirurgias eletivas que atendam à legislação. Este problema é tratado em 2 níveis. O primeiro é relativo ao planejamento e é chamado de Master Surgical Schedule (MSS) que consiste em definir os recursos necessários para a realização de um conjunto de procedimentos. O segundo se refere à programação e é chamado de Surgical Case Assigment Problem (SCAP) e tem por objetivo alocar o médico a cada cirurgia. Assim, foram propostos dois modelos de programação matemática, um para o MSS e outro para o SCAP. Estes modelos foram aplicados no caso real da alocação de residentes para cirurgias vasculares no Hospital Universitário Pedro Ernesto. Como resultado do modelo MSS, identificou-se a necessidade de mais anestesistas e maior disponibilidade de equipamentos para atender à legislação de formação de residentes. Por fim, como resultado do SCAP, o quantitativo de cirurgias foi distribuído entre os cirurgiões de forma balanceada. / [en] Vascular diseases are serious diseases and their treatments are complex and require surgical procedures. In order to perform these procedures, is required equipment, qualified teams and an intensive care unit (ICU) equipped for the postoperative period. Teaching hospitals must comply with current legislation that recommend a minimum number of surgeries for the resident s approval in the training program. Thus, it is proposed, through optimization, to find efficient solutions for the planning and programming of elective surgeries that comply with the legislation. This problem is dealt with on two levels. The first is relative to planning and is called the Master Surgical Schedule (MSS), which consists of defining the necessary resources to perform a set of procedures. The second one refers to programming and is called Surgical Case Assigment Problem (SCAP) and aims to allocate the doctor to each surgery. Thus, two models of mathematical programming were proposed, one for MSS and another for SCAP. These models were applied in the real case of residents allocation for vascular surgeries at Pedro Ernesto University Hospital. As a result of MSS model were identified the need for more anesthesiologists and greater availability of equipment ensure the cover of resident training legislation. Finally, as a result of SCAP, the quantity of surgeries was distributed equitably distributed among surgeons.

This research has been defined in response to the Winnipeg children's hospital (WCH) challenges such as long waiting times, delays and cancellations in surgical flow. Preliminary studies on the surgical flow revealed that definition and implementation of successful process improvement projects (PIPs) along with application of an efficient master surgical schedule (MSS) are efficient solutions to the critical problems in WCH. In the first phase of this work, a process improvement program including three major PIPs, is defined and implemented in WCH in order to improve the efficiency of the processes providing surgical service for patients. In the second phase, two new multi-objective mathematical models are presented to develop efficient MSSs for operating room department (OR) in WCH.

This research has been defined in response to the Winnipeg children's hospital (WCH) challenges such as long waiting times, delays and cancellations in surgical flow. Preliminary studies on the surgical flow revealed that definition and implementation of successful process improvement projects (PIPs) along with application of an efficient master surgical schedule (MSS) are efficient solutions to the critical problems in WCH. In the first phase of this work, a process improvement program including three major PIPs, is defined and implemented in WCH in order to improve the efficiency of the processes providing surgical service for patients. In the second phase, two new multi-objective mathematical models are presented to develop efficient MSSs for operating room department (OR) in WCH.