We develop three novel logic-based Benders' decomposition (LBBD) approaches and a cut propagation mechanism to solve large-scale location-allocation integer programs (IPs). We show that each LBBD can be implemente...
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We develop three novel logic-based Benders' decomposition (LBBD) approaches and a cut propagation mechanism to solve large-scale location-allocation integer programs (IPs). We show that each LBBD can be implemented in four different possible ways, yielding distinct LBBD variants with completely different computational performances. LBBDs decompose the IP model into an integer location and knapsack-based allocation master problem and multiple packing IP sub-problems. We illustrate the performance of our LBBDs on the distributedoperatingroom (OR) scheduling (DORS) problem, where patients and ORs are collaboratively scheduled across a network of hospitals, and the goal is to select patients with the highest priority scores and schedule them in the current planning horizon, while determining the number of surgical suites and operatingrooms required to accommodate the schedule at minimum cost. We quantitatively demonstrate that the new Benders' cuts, cut propagation, and implementation can individually or collectively yield a computational time impact of at least two orders of magnitude. We also demonstrate that our LBBDs are 10-100x faster than IP+Gurobi and are more successful at finding optimal solutions. Finally, we show that DORS increases the average OR utilization across the network to more than 95%. (C) 2016 Elsevier B.V. All rights reserved.
The distributedoperatingroom (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this pr...
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The distributedoperatingroom (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this problem where surgery durations are considered to be uncertain. In order to obtain solutions for the challenging stochastic model, we use sample average approximation and develop two enhanced decomposition frameworks that use logic-based Benders (LBBD) optimality cuts and binary decision diagram based Benders cuts. Specifically, to the best of our knowledge, deriving LBBD optimality cuts in a stochastic programming context is new to the literature. Our computational experiments on a hospital data set illustrate that the stochastic formulation generates robust schedules and that our algorithms improve the computational efficiency. Summary of Contribution: We propose a new model for an important problem in healthcare scheduling, namely, stochastic distributed operating room scheduling, which is inspired by a current practice in Toronto, Ontario, Canada. We develop two decomposition methods that are computationally faster than solving the model directly via a state-of-the-art solver. We present both some theoretical results for our algorithms and numerical results for the evaluation of the model and algorithms. Compared with its deterministic counterpart in the literature, our model shows improvement in relevant evaluation metrics for the underlying scheduling problem. In addition, our algorithms exploit the structure of the model and improve its solvability. Those algorithms also have the potential to be used to tackle other planning and scheduling problems with a similar structure.
operatingroom (OR) scheduling is a challenging combinatorial problem and hence most optimization-based OR scheduling research makes simplifying assumptions for tractability, including deterministic surgical durations...
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In this study, the operatingroomscheduling of hospital networks with virtual alliance has been studied, which at the same time, there is a kind of cooperation and competition among the agents. The main feature in ne...
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In this study, the operatingroomscheduling of hospital networks with virtual alliance has been studied, which at the same time, there is a kind of cooperation and competition among the agents. The main feature in networks with the virtual alliance is the possibility of different objective functions among the agents, which has priority for agents compared to the network's overall objective. Here, by considering the conditions of emergency arrival, the time of inter-hospital transportation, and the elective patients and non-elective patients in the scheduling, an attempt has been made to bring the problem closer to real-world situations. To solve this problem, first, a mixed-integer mathematical programming model is proposed. Because of its NP-hardness, then, a multi-objective learning variable neighborhood search algorithm is designed to minimize total completion of surgeries, the cost of allocating the patient to the hospital and the surgeon, and the cost of overtime operatingrooms throughout the network. Finally, the performance of the proposed algorithm is evaluated in comparison with the NSGA-II and memetic-based algorithm, which due to considering the learning mechanism along with the use of various neighborhood structures in the proposed algorithm, its results are promising. It is expected that by using the proposed algorithms in a cooperative structure, the hospitals are able to achieve optimal/near-optimal solutions in a reasonable time, in which, in addition to more economic activity, patients also benefit due to better use of resources. (C) 2021 Elsevier B.V. All rights reserved.
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