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Solving the planning and scheduling problem simultaneously in a hospital with a bi-layer discrete particle swarm optimization

1 Department of logistics engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China
2 Department of Anesthesiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi’an, China

Special Issues: Optimization methods in Intelligent Manufacturing

The operating room is one of the most capital-intensive resources for a hospital. To achieve further improvements and to restrict cost increases, hospitals may need to operate more efficiently with the resources they already possess. The paper considers the joint problem of planning and scheduling patients in operating rooms on an operational level (weekly basis) with two objectives: maximizing the overall patients’ satisfaction and minimizing the cost of overtime in operating rooms as well as the daily cost of operating rooms and recovery beds, which is NP-hard. The decision problem is solved using a bi-layer discrete particle swarm optimization, introducing a repair mechanism for infeasible solutions, specific operators like crossover, insertion and exchange. Moreover, a gap finding scheduling heuristic is designed to solve the surgical case sequencing problem. We first compare the performance of the proposed solution method to that of Fei et al. for three instances separately, using data of a Chinese hospital. Next, the efficient Pareto solutions for the joint problem are presented. The results show that the bi-layer discrete particle swarm optimization can solve the operating room scheduling efficiently and effectively.
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Keywords operating room scheduling; integrated planning and scheduling problem; bi-layer discrete particle swarm optimization; patients’ satisfaction; operating room cost

Citation: Xiuli Wu, Xianli Shen, Linjuan Zhang. Solving the planning and scheduling problem simultaneously in a hospital with a bi-layer discrete particle swarm optimization. Mathematical Biosciences and Engineering, 2019, 16(2): 831-861. doi: 10.3934/mbe.2019039

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Copyright Info: © 2019, Xiuli Wu, licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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