![]() ![]() The joint optimization problem of multiresource capacity planning and multitype patient scheduling under uncertain demands and random capacity consumption poses a significant computational challenge. The experimental results indicate that the proposed algorithms are considerably more efficient than the traditional ones, and that the resulting schedules of the two-phase optimization model significantly outperform those of a conventional stochastic programming model in terms of the patients' waiting times and the total costs on the long run. We conduct numerical experiments to evaluate the model and algorithms proposed in this thesis. In order to cope with the huge complexity of realistically sized problems, we develop a reinforcement-learning-based approximate dynamic programming algorithm and several column-generation-based heuristic algorithms as the solution approaches. The MDP model in the first phase determines the surgeries to be performed in each week and minimizes the expected total costs over an infinite horizon, then the stochastic programming model in the second phase optimizes the assignments of the selected surgeries to surgical blocks. Considering that the pure mathematical programming models commonly used in literature do not optimize the long-term performance of the surgery schedules, we propose a novel two-phase optimization model that combines Markov decision process (MDP) and stochastic programming to overcome this drawback. The objective is to minimize the patient-related costs incurred by performing and postponing surgeries as well as the hospital-related costs caused by the utilization of surgical resources. In each week, the surgery planner should determine the surgical blocks to open and assign some of the surgeries in the waiting list to the open surgical blocks. ![]() The arrivals of new patients in each week, the duration of each surgery, and the length-of-stay of each patient in the downstream recovery unit are subject to uncertainty. This thesis deals with the advance scheduling of elective surgeries in an operating theatre that is composed of operating rooms and downstream recovery units. (2016)Scenario analysisRizk and Arnaout (2012),Faul et al. (2011), Choi and Wilhelm (2014), Bouguerra et al. (2019) Analytical procedure van Oostrum et al. (2019) Constructive algorithm Denton et al. (2015), Dellaert and Jeunet (2017), Ansarifar et al. (2012), Guido and Conforti (2017) and Ansarifar et al. (2009), Dekhici and Belkadi (2010), van Essen et al. (2014) and Almaneea and Hosny (2019) Tabu search Lamiri et al. (2018) and Jebali and Diabat (2017) Improvement heuristic Simulated annealing Lamiri et al. (2009b), Min and Yih (2014), Silva and de Souza (2020) and Sauré et al. (2016) Dynamic programming Cardoen et al. (2011), Ma and Demeulemeester (2013) and Hashemi Doulabi et al. (2019) Column generation van Oostrum et al. ![]() (2012), Dellaert and Jeunet (2017) and Marques et al. (2017) and Silva and de Souza (2020) Mixed integer programmingPham and Klinkert (2008), Marques et al. (2017) Integer programmingTà nfani and Testi (2010),Addis et al. (2009), Min and Yih (2014) andJebali and Diabat (2017) Mathematical programming Linear programmingAugusto et al. Additionally, the algorithms are shown to be capable of computing high-quality near-optimal policies for realistically sized problems. Experimental results reveal that the proposed algorithms consume much less computation time in comparison with that required by conventional dynamic programming methods. We then develop a novel approximate dynamic programming algorithm based on recursive least-squares temporal difference learning as the solution technique. Considering the curses of dimensionality resulting from the large scale of realistically sized problems, we first analyze the structural properties of the MDP model and propose an algorithm that facilitates the search for greedy actions. By minimizing the cost function of MDP over an infinite horizon, we seek to achieve the best trade-off between the patients' waiting time and the over-utilization of operating rooms and downstream resources. At the end of each week, we select the patients to be treated in the following week from the waiting list. Then taking the random arrivals of patients into account, sequential decisions are made on a weekly basis. To manage elective patients from multiple specialties equitably and efficiently, we establish a waiting list of patients and assign each patient a time-dependent dynamic priority. ![]() In this paper, we propose an approximate dynamic programming algorithm to solve a Markov decision process (MDP) formulation for the admission control of elective patients. ![]()
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