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A particle swarm optimization algorithm on the surgery scheduling problem with downstream process

机译：下游过程的手术调度问题粒子群优化算法

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This paper focuses on generating an optimal surgery schedule of elective-patients in multiple operating theatres, which considering the intra-operative care and recovery phases. We try to determine the surgery sequence and location. With regard to the downstream process, recovery room, the problem is modeled as a hybrid integer program with the objective of minimizing the operating costs of hospital, which includes operating rooms' fixed costs, operating rooms' overtime costs and recovery costs. A discrete particle swarm optimization algorithm combined with heuristic rules is proposed. We text our approach with realistic data, the results show the algorithm we present basically reached the same level of CPLEX, while the computation time is far less than CPLEX. Additionally, the approach can find the optimized number of daily opening operating rooms and recovery beds through varying parameters in the experiment, which give management insights to hospital and reduce the daily operating cost.
机译：本文重点介绍，在多次操作剧院中产生最佳的手术时间表，这考虑了手术内护理和恢复阶段。我们尝试确定手术序列和位置。关于下游过程，恢复室，问题被建模为混合整数程序，目的是最大限度地减少医院的运营成本，包括手术室的固定成本，手术室的加班费和恢复成本。提出了一种离散的粒子群优化算法与启发式规则相结合。我们用现实数据发短信，结果表明我们所呈现的算法基本上达到了相同的CPLEX水平，而计算时间远远低于CPLEX。此外，该方法可以通过实验中的不同参数找到优化的每日开放式手术室和恢复床，从而为医院提供管理见解并降低日常运营成本。

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