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研究生:李岳珉
研究生(外文):Yueh-Min Lee
論文名稱:護理人員排班問題求解之研究
論文名稱(外文):Solution Approaches for the Nurse Rostering Problems
指導教授:王逸琦王逸琦引用關係吳泰熙吳泰熙引用關係
指導教授(外文):Yi-Chi WangTai-Hsi Wu
學位類別:碩士
校院名稱:逢甲大學
系所名稱:工業工程與系統管理學研究所
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:145
中文關鍵詞:粒子群演算法基因演算法護理人員排班問題
外文關鍵詞:Particle Swarm OptimizationGenetic AlgorithmNurse Scheduling ProblemNurse Rostering Problem
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隨著社會大眾對醫療服務品質的要求以及護理人力成本佔總成本比例之日益增加,護理人員排班問題(NSP)之研究逐漸受到重視。護理人員排班為典型之24小時全年無休之範例,除了需要滿足勞基法的規定外,仍需滿足醫療院所各單位之眾多限制,已經被證明為NP-hard問題。護理人員班表之規劃,對於護理服務品質有極大之影響,若以人力排班通常需花費極長之時間,此問題深深困擾著各科護理長及管理階層。目前亟待發展一快速且有效的排班方法,針對問題進行求解,以達降低排班人員負擔並同時增加排班公平性的目標。
  本研究以個案醫院為研究對象,問題由十八條軟性、硬性限制式以及「排班公平性最大化」、「軟性限制式之違反次數最小化」等決策目標所組成。本研究首先應用數學規劃法構建NSP問題之數學模式並以Lingo 8.0進行求解;接著,設計啟發式演算法-基因演算法(GA)以及粒子群演算法(PSO),求解NSP問題。研究過程中,再自行發展的一套下界解程序,推算NSP問題之下界解,提供上述求解方法一比對標竿。啟發式演算法的求解過程中,將以三種不同的初始班表指派模式:(1)原始模式;(2)梯形模式;以及(3)改良式梯形模式,進行問題求解。本研究希望經由上述各項求解成果的比較與分析,找出能有效並迅速求得最適班表的方法。最後,以啟發式演算法為核心,設計合適之人員基本資料管理介面、排班資訊輸入介面以及班表結果輸出介面,提供排班人員一可靠且易於使用的自動化排班系統。
  研究結果顯示,改良式梯形基因演算法的求解效率大幅優於其餘求解方法,且在部分問題中,改良式梯形基因演算法可求得最適解之班表。而本研究所提出的改良式梯形排班模式,除了能有效增加班表的排班公平性外,還能夠提供護理人員一可預測性高且減輕身體不良負荷的人性化班表,實可供排班人員進行排班工作時參考使用。
With people paying more and more attentions to the quality of medical services, the nurse scheduling problem (NSP) has become a conspicuous and important problem nowadays. The nurse scheduling problem has been proved to be an NP-hard problem. Feasible rosterings (solutions) of the NSP must satisfy the regulation of Labor Standards Law of local countries and numerous constraints required by each hospital unit. Preferences from the nurses have to be considered as well while solving the NSPs. The quality of rosterings is going to influence the quality of nurses’ services significantly. Producing rosterings manually by the head nurses or the administrations is a very time consuming task, and they usually get deeply bothered every time they are planning ahead for the rosterings of the next planning period (a week, two weeks, or a month). In order to reduce the heavy working load for the head nurses or the administrations and increase the fairness of rosterings among nurses simultaneously, speedy and effective rostering approaches are thus urgently needed.
In this research, we attempt to solve the NSPs using both mathematical programming (MP) approach and heuristic algorithms ― the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO). Three initial forms of rostering for each heuristic algorithm are designed, namely the random form, the ladder-shaped form, and the improved ladder-shaped form. A procedure for deriving lower bound solutions (LBS) is also presented to serve as the base for comparison, similar to the role of optimal solutions of the NSPs. Several strategies are designed for both the GA and PSO algorithm for solving the NSPs. The computational results are compared and several further analyses are conducted. Computational results point out that the efficiency of GA using the improved ladder-shaped form as the initial solution outperforms the efficiency of the others. In some test problems, the GA with the improved ladder-shaped form even attains the global optimum. Moreover, the improved ladder-shaped form presented in this research not only increases the fairness of rosterings, but also provides nurses with predictable rosterings and more conforming to biological clock.
Finally, an automatic scheduling system is designed by using the proposed GA algorithm as the core program to offer the head nurse an option for producing appropriate rosterings easily and reliably.
致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍 3
1.4 研究流程 3
第二章 文獻回顧 5
2.1 影響護理人員排班之因素 5
2.2 護理人員排班之相關研究 6
2.3 基因演算法 8
2.4 粒子群演算法 12
第三章 個案問題描述及研究設計 16
3.1 護理人員排班問題(NSP) 16
3.2 NSP問題之數學模式建構 20
3.3下界解推導 23
3.4 啟發式演算法之求解模式 31
3.5 研究整體架構 47
第四章 排班個案成果分析 49
4.1 個案醫院之排班單位現況 49
4.2 啟發式演算法執行環境說明及參數設計 49
4.3 NSP問題之成果分析 50
4.4 護理人員排班系統實作 60
第五章 結論 69
5.1 結論 69
5.2 未來研究方向與建議 70
參考文獻 71
附錄A 啟發式演算法成果-第一組決策目標 74
附錄B 各排班單位之最適班表-第一組決策目標 86
附錄C 啟發式演算法成果-第二組決策目標 97
附錄D 各排班單位之最適班表-第二組決策目標 109
附錄E 各排班單位之最適班表-第三組決策目標(GA#3) 114
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