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Support optimal scheduling with weighted random forest for operation resources

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Abstract

Operation-related resources are lots of manpower and material with the characteristics of high cost and high income in hospitals, and scheduling optimization is a very important research issue in medical service. In this paper, to cope with the actualities of operation resources scheduling, such as poor planning, lack of standardized scheduling rules, chaotic use of the operating rooms, and many human interference factors, we propose a systematic approach to optimize scheduling problems based on multiple characteristics of operating resources. We first design a framework that includes the composite dispatching rules (CDR), optimization ideology, and feedback mechanism, in which the CDR integrates flexible operating time, hold-up time of medical facilities, available time of medical staff, and multiple constraints. The optimization ideology is carried out through a learning model based on the weighted random forest (WRF) algorithm. The feedback mechanism enables the approach to realize closed-loop optimizations adaptively. Finally, the superiority of the systematic scheduling approach (SSA) is analyzed through numerical experiments on a simulation platform. Results of the simulation experiments show that the proposed scheduling method can improve performances significantly, especially in the waiting time of patients.

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Acknowledgements

This research was supported by the National Key R&D Program of China (No. 2018YFE0105000), the Shanghai Municipal Commission of Science and Technology (No. 19511132100) and the National Natural Science Foundation of China (No. 51475334).

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Li Li, Qingyun Yu, Haoyi Shi & Yuguang Liu

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  1. Li Li
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  2. Qingyun Yu
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  4. Yuguang Liu
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Correspondence to Li Li.

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Li, L., Yu, Q., Shi, H. et al. Support optimal scheduling with weighted random forest for operation resources. Control Theory Technol. 19, 484–498 (2021). https://doi.org/10.1007/s11768-021-00051-1

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