Strategic Level Proton Therapy Patient Admission Planning: A Markov Decision Process Modeling Approach

A relatively new consideration in proton therapy planning is the requirement that the mix of patients treated from different categories satisfy desired mix percentages. Deviations from these percentages and their impacts on operational capabilities are of particular interest to healthcare planners. In this study, we investigate intelligent ways of admitting patients to a proton therapy facility that maximize the total expected number of treatment sessions (fractions) delivered to patients in a planning period with stochastic patient arrivals and penalize the deviation from the patient mix restrictions. We propose a Markov Decision Process (MDP) model that provides very useful insights in determining the best patient admission policies in the case of an unexpected opening in the facility (i.e., no-shows, appointment cancellations, etc.). In order to overcome the curse of dimensionality for larger and more realistic instances, we propose an aggregate MDP model that is able to approximate optimal patient admission policies using the worded weight aggregation technique. Our models are applicable to healthcare treatment facilities throughout the United States, but are motivated by collaboration with the University of Florida Proton Therapy Institute (UFPTI)

ROBUST RADIOTHERAPY APPOINTMENT SCHEDULING

Optimal scheduling of patients waiting for radiation treatments is a quite challenging operational problem in radiotherapy clinics. Long waiting times for radiotherapy treatments is mainly due to imbalanced supply and demand of radiotherapy services, which negatively affects the effectiveness and efficiency of the healthcare delivered. On the other hand, variations in the time required to set-up machines for each individual patient as well as patient treatment times make this problem even more involved. Efficient scheduling of patients on the waiting list is essential to reduce the waiting time and its possible adverse direct and indirect impacts on the patient. This research is focused on the problem of scheduling patients on a prioritized radiotherapy waiting list while the rescheduling of already booked patients is also possible. The aforementioned problem is formulated as a mixed-integer program that aims for maximizing the number of newly scheduled patients such that treatment time restrictions, scheduling of patients on consecutive days on the same machine, covering all required treatment sessions, as well as the capacity restriction of machines are satisfied. Afterwards, with the goal of protecting the schedule against treatment time perturbations, the problem is reformulated as a cardinality-constrained robust optimization model. This approach provides some insights into the adjustment of the level of robustness of the patients schedule over the planning horizon and protection against uncertainty. Further, three metaheuristics, namely Whale Optimization Algorithm, Particle Swarm Optimization, and Firefly Algorithm are proposed as alternative solution methods. Our numerical experiments are designed based on a case study inspired from a real radiotherapy clinic. The first goal of experiments is to analyze the performance of proposed robust radiotherapy appointment scheduling (ASP) model in terms of feasibility of schedule and the number of scheduled patients by the aid of Monte-Carlo simulation. Our second goal is to compare the solution quality and CPU time of the proposed metaheuristics with a commercial solver. Our experimental results indicate that by only considering half of patients treatment times as worst-case scenario, the schedule proposed by the robust RAS model is feasible in the presence of all randomly generated scenarios for this uncertain parameter. On the other hand, protecting the schedule against uncertainty at the aforementioned level would not significantly reduce the number of scheduled patients. Finally, our numerical results on the three metaheuristics indicate the high quality of their converged solution as well as the reduced CPU time comparing to a commercial solver

Dispatch hydroelectric power plants : implementation with genetic algorithms

Orientador: Paulo de Barros CorreiaDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Esta dissertação de mestrado tem por objetivo apresentar e implementar um modelo de otimização da operação diária das usinas hidrelétricas do Médio São Francisco. O estudo considera oito usinas do sistema - Sobradinho, Luiz Gonzaga, Apolônio Sales, Paulo Afonso I, II, III e IV e Xingó - pertencentes à Companhia Hidro Elétrica do São Francisco. Seu objetivo é maximizar eficiência de geração das usinas e minimizar o número de partidas e paradas de suas unidades eradoras, simultaneamente. A técnica de resolução é feita em duas etapas, sendo que a Etapa 1 determina quanto cada usina deve gerar a cada intervalo de tempo, e a Etapa 2 determina o número de unidades geradoras em operação e a carga de uma usina específica. A formulação matemática do problema proposto é de natureza não linear inteira mista e, para solucionar o modelo foram utilizadas técnicas de Computação evolutiva, em específico os Algoritmos genéticos, e de Programação linear. Esta metodologia foi desenvolvida com dois programas computacionais, ambos comerciais sendo um software com linguagem de programação de quarta geração. Um dos programas foi utilizado para a interface, enquanto no de quarta geração, o modelo de otimização foi implementado. A solução obtida aumenta a eficiência em relação ao despacho atual e em relação as restrições operativas usuais. A aplicabilidade deste modelo pode ser utilizada na otimização de outras usinas em cascataAbstract: This dissertation aims to presents and implement an optimization model for daily operation of Middle São Francisco River hydroeletric system. The study considers eight power plants - Sobradinho, Luiz Gonzaga, Apolônio Sales, Paulo Afonso I, II, III, IV e Xingó - witch belongs to the São Francisco Hydroeletric Company. Its objective is to maximize the power plant efficiency and, simultaneously, to minimize the number of startups and shutdowns of generating units. The technique of resolution is made in two steps: Step 1 determines the load allocated to each power plant at each hour; Step 2 defines the number of generating units in operation and the load of particular power plant. The mathematical formulation is non-linear mixed integer programs and solved with a Genetic Algorithm (GA) approad, and Linear Programming . This model was implemented with two computation programs, One a commercial optimization solver, and a in house GA solver coded with a programming language of fourth generation. One of the programs was used to interface, while the fourth generation, the optimization model was implemented. This solution increases effi- ciency in relation to the actual dispatch and for the usual operational restrictions. The applicability of this model can be used for the optimization of other plants in cascadeMestradoPlanejamento de Sistemas EnergeticosMestre em Planejamento de Sistemas Energético

Optimisation de la trajectoire du patient dans les centres de radiothérapie ou d'hadronthérapie

Ionizing therapy treatment scheduling optimization can improve both patients’ care and care structures’ efficiency. Despite its complexity, mainly because of scarce resources and essential care activities’ repetition, we designed a linear programming model which allows scheduling complex treatment protocols better than existing models on several performance indicators regarding material and human resources while minimizing waiting times and taking into account both patients’ and radiotherapists’ availabilities. Furthermore, we developed two practical applications of heuristic scheduling methods : i) a constructive heuristic scheduling model for the Protontherapy Center of Orsay (CPO) able to plan whole care trajectory base on available resources and ii) an healthcare adaptation of the industrial scheduling platform PREACTOR which achieve linear model's precision resolved through complex heuristics from PREACTORL’optimisation de la planification des traitements par rayons ionisants est bénéfique tant aux patients qu’aux structures de soins bien que particulièrement difficile du fait de la rareté des ressources et de l'importante répétition des séances. Face à cette problématique, un modèle d'optimisation linéaire à nombres entiers a été créé permettant de planifier des protocoles de traitement complexes tout en prenant en compte la disponibilité des patients ainsi que des radiothérapeutes qui les suivent avec pour résultat une amélioration significative des performances sur des indicateurs couvrant les ressources humaines et matérielles ainsi que les délais de prise en charge. De plus nous avons développé des solutions adaptées à des contextes concrets : i) une planification heuristique de la trajectoire des patients au sein du Centre de Protonthérapie d’Orsay (CPO) assortie d'indicateurs de performances, et ii) une adaptation au monde hospitalier de la solution industrielle de planification PREACTOR permettant de conserver la finesse obtenue dans les modélisations linéaires tout en tirant parti des capacités de résolution des heuristiques complexes intégrées à PREACTO