工业工程 ›› 2017, Vol. 20 ›› Issue (4): 49-56,71.doi: 10.3969/j.issn.1007-7375.e17-3054

• 实践与应用 • 上一篇    下一篇

考虑医患满意度和手术成本的日手术排程方法

郝志刚   

  1. 大连理工大学 管理与经济学部, 辽宁 大连 116023
  • 收稿日期:2017-03-13 出版日期:2017-08-30 发布日期:2017-09-08
  • 作者简介:郝志刚(1992-),男,安徽省人,硕士研究生,主要研究方向为医疗健康、服务运作管理
  • 基金资助:

    国家自然科学基金资助项目(71533001);中央高校基本科研业务费专项资金资助项目(DUT15QY32)

A Method of Surgical Scheduling:Improving the Satisfaction of Doctors and Patients while Cutting Operation Costs

HAO Zhigang   

  1. Faculty of Management and Economics, Dalian University of Technology, Dalian 116023, China
  • Received:2017-03-13 Online:2017-08-30 Published:2017-09-08

摘要:

针对手术排程问题涉及众多利益相关者的特点,本文考虑相关医疗资源约束,在手术排程问题中寻求最优的开启手术室数量以降低手术成本,寻求最优的手术顺序以降低病患的等待时间和医生的加班时间,从而提高病患、医生和医院三方满意度,并依此建立了求解多目标手术排程问题的优化模型;根据问题特点,基于改进的非支配排序算子的非支配排序遗传算法(INSGA-Ⅲ)求解问题,并提出种群染色体唯一策略等以进一步提升算法的搜索性能,利用GD和HVR指标检验算法的性能。最后,通过对某三甲医院的日手术排程过程进行仿真实验,验证了所提出的手术排程方法的可行性和有效性。

关键词: 手术排程, 手术成本, 医患满意度, 多目标优化, 改进的非支配排序遗传算法(INSGA-Ⅲ)

Abstract:

Considering that the issue of surgical scheduling involves many stakeholders, and taking the related resource constraints into account, a mathematical model of solving surgical scheduling problem is proposed. It aims to enhance the satisfaction of doctors, patients and the hospital by finding the right number of opening operation rooms to reduce hospital operating costs and the best operation order to reduce waiting time and overtime. According to the characteristics of the issue, an improved non-dominated sorting genetic algorithm Ⅲ is proposed based on improved crowded distance calculating operator. The algorithm also proposes population chromosome unique policy to improve the diversity of the population, using GD and HVR metrics to test the performance of the algorithm, which in turn enhances the algorithm's searching performance. Finally, simulation result based on a third class hospital shows the effectiveness and feasibility of the proposed model.

Key words: surgical scheduling, operation costs, health care satisfaction, multi-objective optimization, improved NSGA-Ⅲ (non-dominated sorting genetic algorithm Ⅲ)

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