人力資源排程系統自動化的實現——以護理人員為例｜國立臺灣科技大學博碩士論文系統

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研究生：	陸佳寧 Jia-Ning Lu
論文名稱：	人力資源排程系統自動化的實現——以護理人員為例 Automatic Human Resource Scheduling System—Case Study of Nursing Staff
指導教授：	呂志豪 Shih-Hao Lu 鄭仁偉 Jen-Wei Cheng
口試委員:	曾盛恕 Seng-Su Tsang 張飛黃 Fei-Huang Chang
學位類別：	碩士 Master
系所名稱：	管理學院 - 企業管理系 Department of Business Administration
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	65
中文關鍵詞：	護理人員排班、規劃求解、公平性排班
外文關鍵詞：	Nurse Scheduling, Solver, Fairness
相關次數：	點閱：470 下載：0
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醫院結構中，護理人員佔總人力資源較大的比例，如何妥善的管理護理人員係為相當重要的議題。實務上，護理人員的班表普遍為護理長手動排班，再由護理人員私下協議做微調。護理長需要在排班時考量所有因素，相當耗時費力，且人工排班在排班作業上缺乏公平基礎，使護理人員易對班表的公平性存有疑慮。
為彌補人工排班之局限性，本研究採用資訊科技相結合的自動化排班法，使用Microsoft Excel中的規劃求解工具箱作為求解工具，以中國大陸寧波某醫院科室作為訪談對象，設定7天為一排班週期，並以「先排休，後排班」的構思作為切入點。在醫院規範、各班別人數要求，以及人員休假天數等限制之下，構建一滿足所有條件的排班系統。此外，本研究亦將護理人員等級、調班情況，以及遭逢天災、人禍等可能影響人員配置的情況列入考量。
經驗證後顯示，本研究所構建之排班系統可順利生成一具有實務適用性的班表。所生成之班表兼具公平性與正確性等優點，且順利將排班的時間控制在二十秒左右，成功解決了人工手動排班的耗時問題。實務上每間醫院及其各科室皆有自身須滿足的條件，未來研究可以本研究發展之系統作為參考，並加以調整及優化。

關鍵字：護理人員排班、規劃求解、公平性班表

Nurses take a considerable part of overall human resources in a hospital. In practice, the nursing staff's schedule is generally manual scheduling for the head nurse, and then fine-tuned by the nursing staff in private agreement. How to properly schedule these labor forces has always been a difficult task for the head nurses since they have to understand needs of all the nurses and ensure both the efficiency and fairness at the same time.
The aim of this study is to develop an automatic scheduling algorithm in order to save labor for making schedules for all the nurses while ensure the efficiency and fairness. The scheduling algorithm is developed based on ‘Solver’ toolbox in Microsoft Excel and the basic logic of this algorithm is to figure out the day off of nurses first and then schedule the work days. Hospital regulations and number of nurses of each shift are considered and set as the boundary conditions. Factors that influence the scheduling, for example rank of nurse, internally work shift, extreme weathers and hazards are all included in the algorithm. Survey is conducted in a particular hospital located in Ningbo China in order to build and verify the algorithm.
Via verification, the algorithm works well for this particular hospital with a normal timetable generation time of around 20 seconds given all input data well collected. The author also suggests the algorithm developed in this study might be adjusted and works for other hospitals as well with proper modifications made.

Key Words：Nurse Scheduling、Solver、Fairness

Contents
誌 謝    I
摘 要    II
Abstract    III
Contents    IV
Tables    VI
Figures    VI
Chapter I Introduction    1
1.1 Research Motivation    1
1.2 Research Object    5
1.3 Research Objective    6
1.4 Research Method    7
1.5 Research Process Framework    8
Chapter II Literature Review    9
2.1 Definition of Scheduling Issue    9
2.2 Type of Scheduling    9
2.3 Nurse Scheduling Mode    10
2.4 Nurse Scheduling Criteria    17
2.5 Summary    19
Chapter III Research Content and Method    21
3.1 Research Method    21
3.2 Overview of Nurse Scheduling    23
3.3 Scheduling Rules and Interview Contents    25
3.4 Arrangement of Limited Conditions    29
3.5 Presupposition of Individual Case Research Method    37
Chapter IV Modeling and Research Results    38
4.1 Build Basic Data    38
4.2 Assign Nurses Requiring for Off-day    39
4.3 Create An Off-day Scheduling Table    40
4.4 Create A Daily Shift Scheduling Table    41
4.5 Validation of Research Results    47
4.6 Validation of Effectiveness    48
Chapter V Conclusions and Suggestions    51
5.1 Conclusions    51
5.2 Suggestions    54
Reference    56

Tables
Table 4-1 Construction of Basic Data    38
Table 4-2 Construction of Basic Data    39
Table 4-3 Completed Off-day Intention Questionnaire    40
Table 4-4 Completed Off-day Scheduling Table    41
Table 4-5 Programming Solution Result I    43
Table 4-6 Programming Solution Result II    43
Table 4-7 Preliminary Shift Scheduling Table (without noon shift & flexible nurses)    44
Table 4-8 Screened Noon Shift (73-shift) Scheduling    45
Table 4-9 Scheduling Table with Noon Shift (73-shift) Included    46
Table 4-10 Screened Flexible Shift Nurse Scheduling    47
Table 4-11 Scheduling Table with Flexible Shift Included    47
Table 4-12 Final Scheduling Table    48
Table 4-13 Final Shift Scheduling Table    48
Table 5-14 Accuracy Statistics    50
Table 5-15 Time-consuming Statistics    50

Figures
Figure 1-1 Structure Chart of Research Process    8
Figure 3‑1 Day Shift Nurses    28
Figure 3‑2 Night Shift Nurses    28

                                

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