Applying optimization models in the scheduling of medical exams

The management of waiting lists in hospitals is a topic with relevance given its direct implication in the quality of healthcare services provided to the patients in the good management of human, material and financial resources. Ministry of Health in Portugal stipulates a guaranteed maximum response time for the execution of Complementary Means of Diagnosis and Therapeutics (CMDT), surgeries and outpatient appointments. This paper addresses an investigation conducted at the Centro Hospitalar e Universitário do Porto (CHUP) with the goal of optimizing decisions in the management of waiting lists for CMDT. This objective will be achieved through the development of hill climbing and simulated annealing models. With this study, it was possible to optimize the way these exams can be scheduled, reducing waiting lists, associated costs and waste, improving the quality of service provided to patients.FCT -Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Optimisation stochastique de problèmes d’ordonnancement en santé

RÉSUMÉ : Les problèmes d'ordonnancement en santé sont complexes, car ils portent sur la fabrication d'ordonnancements qui absorbent les perturbations survenant dans le futur. Par exemple, les nouveaux patients urgents ont besoin d’être intégrés rapidement dans le planning courant. Cette thèse s'attaque à ces problèmes d'ordonnancement en santé avec de l'optimisation stochastique afin de construire des ordonnancements flexibles. Nous étudions en premier lieu la fabrication d'horaires pour deux types d’équipes d’infirmières: l’équipe régulière qui s'occupe des unités de soins et l’équipe volante qui couvre les pénuries d’infirmières à l’hôpital. Quand les gestionnaires considèrent ce problème, soit ils utilisent une approche manuelle, soit ils investissent dans un logiciel commercial. Nous proposons une approche heuristique simple, flexible et suffisamment facile à utiliser pour être implémentée dans un tableur et qui ne requiert presque aucun investissement. Cette approche permet de simplifier le processus de fabrication et d'obtenir des horaires de grande qualité pour les infirmières. Nous présentons un modèle multi-objectif, des heuristiques, ainsi que des analyses pour comparer les performances de toutes ces méthodes. Nous montrons enfin que notre approche se compare très bien avec un logiciel commercial (CPLEX), peut être implémentée à moindre coût, et comble finalement le manque de choix entre les solutions manuelles et les logiciels commerciaux qui coûtent extrêmement cher. Cette thèse s'attaque aussi à l'ordonnancement des chirurgies dans un bloc opératoire, fonctionnant avec un maximum de deux chirurgiens et de deux salles, en tenant compte de l'incertitude des durées d'opérations. Nous résolvons en premier lieu une version déterministe, qui utilise la programmation par contraintes, puis une version stochastique, qui encapsule le programme précédent dans un schéma de type ``sample average approximation''. Ce schéma produit des plannings plus robustes qui s’adaptent mieux aux variations des durées de chirurgies. Cette thèse présente le problème de prise de rendez-vous en temps réel dans un centre de radiothérapie. La gestion efficace d'un tel centre dépend principalement de l'optimisation de l'utilisation des machines de traitement. En collaboration avec le Centre Intégré de Cancérologie de Laval, nous faisons la planification des rendez-vous patients en tenant compte de leur priorité, du temps d'attente maximale et de la durée de traitement, le tout en intégrant l'incertitude reliée à l'arrivée des patients au centre. Nous développons une méthode hybride alliant optimisation stochastique et optimisation en temps réel pour mieux répondre aux besoins de planification du centre. Nous utilisons donc l'information des arrivées futures de patients pour dresser le portrait le plus fidèle possible de l'utilisation attendue des ressources. Des résultats sur des données réelles montrent que notre méthode dépasse les stratégies typiquement utilisées dans les centres. Par la suite, afin de proposer un algorithme stochastique et en temps réel pour des problèmes d'allocation de ressources, nous généralisons et étendons la méthode hybride précédente. Ces problèmes sont naturellement très complexes, car un opérateur doit prendre dans un temps très limité des décisions irrévocables avec peu d'information sur les futures requêtes. Nous proposons un cadre théorique, basé sur la programmation mathématique, pour tenir compte de toutes les prévisions disponibles sur les futures requêtes et utilisant peu de temps de calcul. Nous combinons la décomposition de Benders, qui permet de mesurer l'impact futur de chaque décision, et celle de Dantzig-Wolfe, qui permet de s'attaquer à des problèmes combinatoires. Nous illustrons le processus de modélisation et démontrons l’efficacité d'un tel cadre théorique sur des données réelles pour deux applications: la prise de rendez-vous et l'ordonnancement d'un centre de radiothérapie, puis l'assignation de tâches à des employés et leur routage à travers l’entrepôt.----------ABSTRACT : Scheduling problems are very challenging in healthcare as they must involve the production of plannings that absorb perturbations which arise in the future. For example, new high-priority patients needs to be quickly added in the computed plannings. This thesis tackles these scheduling problems in healthcare with stochastic optimization such as to build flexible plannings. We first study the scheduling process for two types of nursing teams, regular teams from care units and the float team that covers for shortages in the hospital. When managers address this problem, they either use a manual approach or have to invest in expensive commercial tool. We propose a simple heuristic approach, flexible and easy enough to be implemented on spreadsheets, and requiring almost no investment. The approach leads to streamlined process and higher-quality schedules for nurses. %improves both the process and the quality of the resulting schedule. The multi-objective model and heuristics are presented, and additional analysis is performed to compare the performance of the approach. We show that our approach compares very well with an optimization software (CPLEX solver) and may be implemented at no cost. It addresses the lack of choice between either manual solution method or a commercial package at a high cost. This thesis tackles also the scheduling of surgical procedures in an operating theatre containing up to two operating rooms and two surgeons. We first solve a deterministic version that uses the constraint programming paradigm and then a stochastic version which embeds the former in a sample average approximation scheme. The latter produces more robust schedules that cope better with the surgeries' time variability. This thesis presents an online appointment booking problem for a radiotherapy center. The effective management of such facility depends mainly on optimizing the use of the linear accelerators. We schedule patients on these machines taking into account their priority for treatment, the maximum waiting time before the first treatment, and the treatment duration. We collaborate with the Centre Intégré de Cancérologie de Laval to determine the best scheduling policy. Furthermore, we integrate the uncertainty related to the arrival of patients at the center. We develop a hybrid method combining stochastic optimization and online optimization to better meet the needs of central planning. We use information on the future arrivals of patients to provide an accurate picture of the expected utilization of resources. Results based on real data show that our method outperforms the policies typically used in treatment centers. We generalize and extend the previous hybrid method to propose a general online stochastic algorithm for resource allocation problems. These problems are very difficult in their nature as one operator should take irrevocable decisions with a limited (or inexistent) information on future requests and under a very restricted computational time. We propose a mathematical programming-based framework taking advantage of all available forecasts of future requests and limited computational time. We combine Benders decomposition, which allows to measure the expected future impact of each decision, and Dantzig-Wolfe decomposition, which can tackle a wide range of combinatorial problems. We illustrate the modelling process and demonstrate the efficiency of this framework on real data sets for two applications: the appointment booking and scheduling problem in a radiotherapy center and the task assignment and routing problem in a warehouse

Prescriptive analytics na gestão de listas de espera hospitalares

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de InformaçãoA gestão de listas de espera em meio hospitalar é um tema de particular relevância dada a sua implicação direta na qualidade dos serviços de saúde prestados aos pacientes, na boa gestão dos recursos humanos, materiais e financeiros e, por último, na regulamentação emanada pelo Ministério da Saúde. Esta regulamentação estipula um tempo máximo de resposta garantido para a execução de Meios Complementares de Diagnóstico e Terapêutica (MCDT), cirurgias e consultas externas. Esta dissertação, intitulada “Prescriptive Analytics na Gestão de Listas de Espera Hospitalares” tem como principal objetivo otimizar a decisão na área da gestão das listas de espera para MCDT. Este objetivo será alcançado através do desenvolvimento de modelos aptos para melhorar os agendamentos, de modo a reduzir os tempos das listas de espera e o desperdício de recursos. Estes modelos têm ainda de ser capazes de integrar um sistema de Adaptive Business Inteligence. É evidente a importância da contribuição que este projeto poderá trazer para este campo de atuação, uma vez que pode servir de auxílio na tomada de decisões clínicas e administrativas, conseguindo atingir benefícios como a diminuição das listas de espera, melhorias na qualidade do serviço prestado e a diminuição dos custos e desperdícios. Para tal, são usadas as metodologias Design Science Research e Cross Industry Standard Process for Data Mining. De igual modo, são apresentados os principais conceitos inerentes a esta área de conhecimento, bem como trabalhos relacionados, que auxiliam na compreensão das abordagens que estão a ser utilizadas, bem como na perceção das restrições e problemas encontrados. Em seguida, é apresentado o desenvolvimento deste projeto e a análise dos seus resultados. Finalmente, na conclusão, está presente uma síntese desta dissertação, as suas contribuições e o trabalho futuro.The management of waiting lists in hospitals is a topic of particular relevance, given its direct implication in the quality of health services provided to the patients, in the good management of human, material and financial resources and, finally, in the regulations issued by the Ministry of Health. This regulation stipulates a guaranteed maximum response time for the execution of Complementary Means of Diagnosis and Therapeutics, surgeries and outpatient appointments. This dissertation, entitled "Prescriptive Analytics for Managing Hospital Waiting Lists" has as its main objective the optimization of the decisions in the area of management of waiting lists for MCDT. This objective will be achieved through the development of models capable of improving scheduling, in order to reduce waiting list times and wasted resources. These models also need to be capable of integrating an Adaptive Business Intelligence system. The importance of the contribution that this project can bring to this field of action is evident, as it can help in clinical and administrative decision-making, achieving benefits such as the reduction of waiting lists, improvements in the quality of the service provided and the reduction of costs and waste. For this purpose, Design Science Research and Cross-Industry Standard Process for Data Mining are used as methodologies. The main concepts inherent in this area of knowledge are also presented, as well as related works, which helped to understand the approaches that are being used, as well as in the perception of restrictions and problems encountered. Then, the development of this project and the analysis of its results are presented. Finally, at the conclusion, there is a summary of this dissertation, as well as its contributions and future work

Optimization methods for the operating room management under uncertainty: stochastic programming vs. decomposition approach

The operating theatres are the engine of the hospitals; proper management of the operating rooms and its staff represents a great challenge for managers and its results impact directly in the budget of the hospital. This work presents a MILP model for the efficient schedule of multiple surgeries in Operating Rooms (ORs) during a working day. This model considers multiple surgeons and ORs and different types of surgeries. Stochastic strategies are also implemented for taking into account the uncertain in surgery durations (pre-incision, incision, post-incision times). In addition, a heuristic-based methods and a MILP decomposition approach is proposed for solving large-scale ORs scheduling problems in computational efficient way. All these computer-aided strategies has been implemented in AIMMS, as an advanced modeling and optimization software, developing a user friendly solution tool for the operating room management under uncertainty