Planification des blocs opératoires avec prise en compte des aléas

148 pagesFacing ever increasing health care demand, limited government support and increasing competition, hospitals are more and more aware of the need to use their resources as efficiently as possible. Operating Rooms (ORs) are among the most critical resources that generate highest costs for a hospital. For these reasons, planning OR activities has become one of the major priorities for hospitals. The planning problem consists of determining a plan that specifies the set of elective patients that would be operated in each OR in each period over a planning horizon. This problem has been addressed in the health care literature and several approaches for OR planning have been proposed. However, most existing approaches are based on deterministic models that do not consider uncertainty related to the ORs' environment. Yet, uncertainty is inherent to the world of health care; it concerns essentially emergency patients' arrivals, surgery durations, and equipments and medical staff availability. The goal of our research is to develop optimization models and solution approaches (algorithms) for ORs planning under uncertainties. In this thesis, we proposed several OR planning models that (i) capture essential factors relevant to surgical activities planning, (ii) explicitly take into account several kinds of uncertainties such as random demand for emergency surgery and random surgery durations, and (iii) can be easily extended to consider other real world constraints. We have also developed several solution methods and approaches for the stochastic ORs planning problem. Using numerical experiments, we have evaluated the performances of the different solution approaches and showed their efficiency. Numerical experiments also show the importance of explicit modeling of uncertainties. Compared with deterministic OR planning models, those neglect uncertainty, our stochastic planning methods yield to significant cost reductions.Le bloc opératoire constitue l'un des secteurs les plus coûteux et les plus importants dans un établissement hospitalier. Afin, d'utiliser de manière efficace et rationnelle les ressources (humaines et matérielles) disponibles tout en assurant une bonne qualité de service vis-à-vis des patients, la planification du bloc opératoire est devenue l'une des premières préoccupations des établissements hospitaliers. Le problème de planification du bloc opératoire consiste à déterminer pour un horizon de plusieurs jours, l'ensemble d'interventions qui seront réalisées dans chaque salle opératoire. Ce problème a été traité dans la littérature et plusieurs approches de planification ont été proposées. Toutefois, les approches existantes sont essentiellement basées sur des modèles déterministes qui font abstraction de toutes sortes d'aléas. Or, le bloc opératoire est sujet à nombreuses formes d'aléas qui concernent essentiellement la chirurgie d'urgence et les durées d'interventions. À ce titre, l'objectif de notre travail de recherche est de développer des modèles et méthodes pour la planification des activités chirurgicales dans le bloc opératoire tout en tenant compte des aléas importants. Dans le cadre de cette thèse, nous avons proposé des modèles stochastiques qui (i) capturent les éléments essentiels à considérer lors de la planification des activités chirurgicales, (ii) permettent de modéliser de manière explicite différentes formes d'aléas tels que les incertitudes relatives aux durées des interventions et à la chirurgie d'urgence, et (iii) peuvent être facilement étendus pour modéliser d'autres contraintes dues aux pratiques du terrain. Nous avons aussi développé plusieurs approches et méthodes d'optimisation complètes et complémentaires pour la planification stochastique du bloc opératoire. Moyennant des expérimentations numériques, nous avons évalué les performances de ces différentes approches et nous avons montré leurs efficacités. En particulier, nous avons montré que des gains considérables peuvent être réalisés moyennant une modélisation stochastique du problème de planification du bloc opératoire

Planification des blocs opératoires avec prise en compte des aléas

148 pagesFacing ever increasing health care demand, limited government support and increasing competition, hospitals are more and more aware of the need to use their resources as efficiently as possible. Operating Rooms (ORs) are among the most critical resources that generate highest costs for a hospital. For these reasons, planning OR activities has become one of the major priorities for hospitals. The planning problem consists of determining a plan that specifies the set of elective patients that would be operated in each OR in each period over a planning horizon. This problem has been addressed in the health care literature and several approaches for OR planning have been proposed. However, most existing approaches are based on deterministic models that do not consider uncertainty related to the ORs' environment. Yet, uncertainty is inherent to the world of health care; it concerns essentially emergency patients' arrivals, surgery durations, and equipments and medical staff availability. The goal of our research is to develop optimization models and solution approaches (algorithms) for ORs planning under uncertainties. In this thesis, we proposed several OR planning models that (i) capture essential factors relevant to surgical activities planning, (ii) explicitly take into account several kinds of uncertainties such as random demand for emergency surgery and random surgery durations, and (iii) can be easily extended to consider other real world constraints. We have also developed several solution methods and approaches for the stochastic ORs planning problem. Using numerical experiments, we have evaluated the performances of the different solution approaches and showed their efficiency. Numerical experiments also show the importance of explicit modeling of uncertainties. Compared with deterministic OR planning models, those neglect uncertainty, our stochastic planning methods yield to significant cost reductions.Le bloc opératoire constitue l'un des secteurs les plus coûteux et les plus importants dans un établissement hospitalier. Afin, d'utiliser de manière efficace et rationnelle les ressources (humaines et matérielles) disponibles tout en assurant une bonne qualité de service vis-à-vis des patients, la planification du bloc opératoire est devenue l'une des premières préoccupations des établissements hospitaliers. Le problème de planification du bloc opératoire consiste à déterminer pour un horizon de plusieurs jours, l'ensemble d'interventions qui seront réalisées dans chaque salle opératoire. Ce problème a été traité dans la littérature et plusieurs approches de planification ont été proposées. Toutefois, les approches existantes sont essentiellement basées sur des modèles déterministes qui font abstraction de toutes sortes d'aléas. Or, le bloc opératoire est sujet à nombreuses formes d'aléas qui concernent essentiellement la chirurgie d'urgence et les durées d'interventions. À ce titre, l'objectif de notre travail de recherche est de développer des modèles et méthodes pour la planification des activités chirurgicales dans le bloc opératoire tout en tenant compte des aléas importants. Dans le cadre de cette thèse, nous avons proposé des modèles stochastiques qui (i) capturent les éléments essentiels à considérer lors de la planification des activités chirurgicales, (ii) permettent de modéliser de manière explicite différentes formes d'aléas tels que les incertitudes relatives aux durées des interventions et à la chirurgie d'urgence, et (iii) peuvent être facilement étendus pour modéliser d'autres contraintes dues aux pratiques du terrain. Nous avons aussi développé plusieurs approches et méthodes d'optimisation complètes et complémentaires pour la planification stochastique du bloc opératoire. Moyennant des expérimentations numériques, nous avons évalué les performances de ces différentes approches et nous avons montré leurs efficacités. En particulier, nous avons montré que des gains considérables peuvent être réalisés moyennant une modélisation stochastique du problème de planification du bloc opératoire

Optimization methods for a stochastic surgery planning problem

International audienceThe purpose of this paper is to propose and compare several optimization methods for elective surgery planning when operating room (OR) capacity is shared by elective and emergency surgery. The planning problem is considered as a stochastic optimization problem in order to minimize expected overtime costs and patients' related costs. An "almost" exact method combining Monte Carlo simulation and mixed integer programming is presented, and its convergence properties are investigated. Several heuristic and meta-heuristic methods are then proposed. Numerical experimentations are conducted to compare the performance of different optimization methods

A stochastic model for operating room planning with elective and emergency surgery demands, OR for Health Policy: Making better decision

International audienc

Column generation approach to operating theater planning with elective and emergency patients

International audienceThe elective surgery planning problem for operating rooms shared between elective and emergency patients is addressed. The planning problem consists in determining the set of elective patients to be operated on in each operating room in each period over a planning horizon in order to minimize patient-related costs and the expected operating rooms' utilization costs. A stochastic mathematical programming model and a column generation approach are proposed. The proposed approach results in both a near-optimal solution and a lower bound to assess the degree of optimality. Solutions within 2% of the optimum are obtained in a short computation time for problems of practical sizes with 12 operating rooms and about 210 elective patients

Optimization methods for a stochastic surgery planning problem

The purpose of this paper is to propose and compare several optimization methods for elective surgery planning when operating room (OR) capacity is shared by elective and emergency surgery. The planning problem is considered as a stochastic optimization problem in order to minimize expected overtime costs and patients' related costs. An "almost" exact method combining Monte Carlo simulation and mixed integer programming is presented, and its convergence properties are investigated. Several heuristic and meta-heuristic methods are then proposed. Numerical experimentations are conducted to compare the performance of different optimization methods.Operating room Surgery planning Emergency Stochastic programming Monte Carlo simulation Heuristics

A stochastic model for operating room planning with elective and emergency surgery demands

http://www.emse.fr/spip/IMG/pdf/RR_2005-500-014.pdfThis paper proposes a stochastic model for Operating Rooms (ORs) planning with two types of surgery demands: elective surgery and emergency surgery. Elective cases can be planned starting from an earliest date with a patient related cost depending on the surgery date. Emergency cases arrive randomly and have to be performed on the day of arrival. The planning problem consists of assigning elective cases to different periods over a planning horizon in order to minimize the sum of elective patients related costs and overtime costs of operating rooms. A new stochastic mathematical programming model is proposed. More specific, we propose a Monte Carlo optimisation method combining Monte Carlo simulation and Mixed Integer Programming. The solution of this method is proved to converge to a real optimum as the computation budget increases. The optimization method is implemented and numerical results are presented

A stochastic model for operating room planning with elective and emergency demand for surgery

International audienceThis paper describes a stochastic model for Operating Room (OR) planning with two types of demand for surgery: elective surgery and emergency surgery. Elective cases can be planned ahead and have a patient-related cost depending on the surgery date. Emergency cases arrive randomly and have to be performed on the day of arrival. The planning problem consists in assigning elective cases to different periods over a planning horizon in order to minimize the sum of elective patient related costs and overtime costs of operating rooms. A new stochastic mathematical programming model is first proposed. We then propose a Monte Carlo optimization method combining Monte Carlo simulation and Mixed Integer Programming. The solution of this method is proved to converge to a real optimum as the computation budget increases. Numerical results show that important gains can be realized by using a stochastic OR planning model

A stochastic model for operating room planning with elective and emergency surgery demands

http://www.emse.fr/spip/IMG/pdf/RR_2005-500-014.pdfThis paper proposes a stochastic model for Operating Rooms (ORs) planning with two types of surgery demands: elective surgery and emergency surgery. Elective cases can be planned starting from an earliest date with a patient related cost depending on the surgery date. Emergency cases arrive randomly and have to be performed on the day of arrival. The planning problem consists of assigning elective cases to different periods over a planning horizon in order to minimize the sum of elective patients related costs and overtime costs of operating rooms. A new stochastic mathematical programming model is proposed. More specific, we propose a Monte Carlo optimisation method combining Monte Carlo simulation and Mixed Integer Programming. The solution of this method is proved to converge to a real optimum as the computation budget increases. The optimization method is implemented and numerical results are presented

Planification des blocs opératoires avec prise en compte des aléas

Dans le cadre de cette thèse, nous avons proposé des modèles stochastiques qui (i) capturent les éléments essentiels à considérer lors de la planification des activités chirurgicales, (ii) permettent de modéliser de manière explicite différentes formes d aléas tels que les incertitudes relatives aux durées des interventions et à la chirurgie d urgence, et (iii) peuvent être facilement étendus pour modéliser d autres contraintes dues aux pratiques du terrain. Nous avons aussi développé plusieurs approches et méthodes d optimisation complètes et complémentaires pour la planification stochastique du bloc opératoire. Moyennant des expérimentations numériques, nous avons évalué les performances de ces différentes approches et nous avons montré leurs efficacités. En particulier, nous avons montré que des gains considérables peuvent être réalisés moyennant une modélisation stochastique du problème de planification du bloc opératoire.In this thesis, we proposed several OR planning models that (i) capture essential factors relevant to surgical activities planning, (ii) explicitly take into account several kinds of uncertainties such as random demand for emergency surgery and random surgery durations, and (iii) can be easily extended to consider other real world constraints. We have also developed several solution methods and approaches for the stochastic ORs planning problem. Using numerical experiments, we have evaluated the performances of the different solution approaches and showed their efficiency. Numerical experiments also show the importance of explicit modeling of uncertainties. Compared with deterministic OR planning models, those neglect uncertainty, our stochastic planning methods yield to significant cost reductions.ST ETIENNE-ENS des Mines (422182304) / SudocSudocFranceF