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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 楊烽正(Feng-Cheng Yang) | |
dc.contributor.author | Yung-Hsuan Lin | en |
dc.contributor.author | 林詠軒 | zh_TW |
dc.date.accessioned | 2021-06-15T16:19:08Z | - |
dc.date.available | 2022-08-01 | |
dc.date.copyright | 2020-08-07 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-06 | |
dc.identifier.citation | Ala, A. and F. Chen (2019). 'Alternative mathematical formulation and hybrid meta-heuristics for patient scheduling problem in health care clinics.' Neural Computing and Applications 32(13): 8993-9008.
Cardoen, B., E. Demeulemeester and J. Beliën (2010). 'Operating room planning and scheduling: A literature review.' European Journal of Operational Research 201(3): 921-932. Chern, C.-C., P.-S. Chien and S.-Y. Chen (2008). 'A heuristic algorithm for the hospital health examination scheduling problem.' European Journal of Operational Research 186: 1137-1157. Deep, K. and H. Mebrathu (2011). 'Combined Mutation Operators of Genetic Algorithm for the Travelling Salesman Problem.' IJCOPI 2: 2-24. Gen, M. and R. Cheng (1997). Genetic algorithms and engineering optimization. New York, John Wiley Sons. Ghaheri, A., S. Shoar, M. Naderan and S. S. Hoseini (2015). 'The Applications of Genetic Algorithms in Medicine.' Oman Med J 30(6): 406-416. Goldberg, D. and J. Holland (1988). 'Genetic Algorithms and Machine Learning.' Machine Learning 3. Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning, Addison-wesley Reading Menlo Park. Guido, R. and D. Conforti (2017). 'A hybrid genetic approach for solving an integrated multi-objective operating room planning and scheduling problem.' Computers Operations Research 87: 270-282. Gul, S., B. T. Denton, J. W. Fowler and T. Huschka (2011). 'Bi-Criteria Scheduling of Surgical Services for an Outpatient Procedure Center.' Production and Operations Management 20(3): 406-417. Harzi, M., J.-F. Condotta, I. Nouaouri and S. Krichen (2017). 'Scheduling Patients in Emergency Department by Considering Material Resources.' Procedia Computer Science 112: 713-722. Holland, J. (1975). 'Adaptation In Natural And Artificial Systems.' University of Michigan Press. Lee, C. K. M. and M. C. Cheng (2018). Appointment Scheduling Optimization for Specialist Outpatient Services. Leemis, L. and S. Park (1998). 'Discrete-Event Simulation: A First Course.' 9: 9-1997. Liu, D. and N. Geng (2020). Stochastic Health Examination Scheduling Problem based on Genetic Algorithm and Simulation Optimization. 2020 IEEE 7th International Conference on Industrial Engineering and Applications (ICIEA). Marynissen, J. and E. Demeulemeester (2019). 'Literature review on multi-appointment scheduling problems in hospitals.' European Journal of Operational Research 272(2): 407-419. Matta, M. E. (2009). 'A genetic algorithm for the proportionate multiprocessor open shop.' Computers Operations Research 36(9): 2601-2618. Munavalli, J. R., S. V. Rao, A. Srinivasan and G. G. van Merode (2020). 'Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times.' Health Informatics J 26(1): 435-448. Pham, D.-N. and A. Klinkert (2008). 'Surgical case scheduling as a generalized job shop scheduling problem.' European Journal of Operational Research 185(3): 1011-1025. Syswerda, G. (1989). Uniform crossover in genetic algorithms. the Third International Conference on Genetic Algorithms Vrugt, M. (2016). Efficient healthcare logistics with a human touch. Zhang, X. (2018). 'Application of discrete event simulation in health care: a systematic review.' BMC Health Serv Res 18(1): 687. 張冠群 (2010). 醫療健檢排程問題及其遺傳演算優化法, 國立臺灣大學. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52576 | - |
dc.description.abstract | 健檢中心常以人工方式進行健康檢查排程,在時間有限及諸多限制下,難以有效排出適當的排程。過往文獻也都以靜態排程且不考量受檢者於診間之間的行走時間為主要探討內容。本研究完整定義了具多重限制式的動態醫療健檢排程問題,建立完整的數學模式。健檢排程的限制有前行健檢項目限制、接續健檢項目限制、時間間隔健檢項目限制、及診間醫療資源數限制等。本研究考量受檢者行於診間之間行走的時間,且全盤考量了受檢者從報到開始直到結帳離院的完整健檢流程,能更貼近健檢中心的實務運作。本研究針對此健檢排程問題研擬一啟發式排程演算程序與一套使用遺傳演算優化的求解系統。採用三個優化子目標,分別是受檢者的候檢和行走時間平均、診間閒置時間平均、及診間完診時間平均等三個子目標值的加總為目標函數值來衡量排程品質。排程目標是達到各子目標均望小的最佳排程。求解的範例數據來自國內某健檢中心的實際數據組成標竿問題,作為不同求解模式的求解範例,以分析與討論不同求解模式的成效。 此外,健檢中心的實務運作可能發生受檢者早到、遲到、檢查項目診查提早或延後結束等隨機情況,此時系統狀態會發生變化。排程系統須依狀態的變化進行重排程,否則實用性不高。本研究將使用離散事件模擬真實健檢中心實務作業產生的系統狀態改變進行排程,以展示系統使用實用性。結果顯示動態排程功能可在受檢者到達診間或結束檢塊診查時,根據目前系統狀態進行重排程,緊密使用醫療資源。求解結果也皆較初排程結果佳,顯示動態排程模式能求得更佳的排程結果。 | zh_TW |
dc.description.abstract | The health inspection center often schedules the health inspection manually. According to the limited time and numerous constrains, it is hardly to schedule properly. The previous literature also focused on static scheduling and did not consider walking time of examinees. This research completely defines the dynamic health inspection scheduling problem with multiple constraints, and establishes a complete mathematical model. Constraints in health inspection scheduling include the precedent constraint, subsequent constraints, time interval constraints, and number of medical resources constraints. This study also considers the walking time taken by the examinees between clinics, and fully considers the complete health inspection process from beginning of registration to leaving the hospital, which can be closer to the practical operation of the health inspection center. This research develops a heuristic scheduling algorithm and a genetic algorithm for this health inspection scheduling problem. This research involves three sub-objectives, which are average waiting and walking time, average idle time, and average makespan. The weighted sum of these three sub-objectives drives the scheduling results to achieve the best schedule. The sample data comes from actual data from a domestic health inspection center to form a benchmark problem, which is used to analyze the effects of different methods. In addition, the actual operation of the health inspection center may occur in random situations such as early or late arrival of examinees, early or late end of diagnosing, and the system status will change at this time. The scheduling system must reschedule according to the status changes to meet the practical situation. This study uses discrete event simulation to simulate the system status changes and reschedule immediately to create a dynamic scheduling system. The results show that the dynamic scheduling system which reschedules according to the current status changes is better than the static(initial) scheduling results, showing that the dynamic scheduling system can obtain better scheduling results. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T16:19:08Z (GMT). No. of bitstreams: 1 U0001-0608202015225300.pdf: 2598773 bytes, checksum: 6fd1021601975896c8b1933441a0a589 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 致謝 I 摘要 II Abstract III 目錄 V 圖目錄 VII 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 3 1.3 研究方法與流程 3 第二章 文獻探討 5 2.1 醫療健檢排程相關問題 5 2.1.1 醫療排程相關問題及文獻 5 2.1.2 離散事件模擬於醫療排程的應用 7 2.2 小結 8 第三章 遺傳演算法 9 3.1 概念 9 3.2 演算流程 9 第四章 離散事件模擬 13 4.1 特性 13 4.2 演算流程 14 第五章 醫療健檢之動態排程演算系統 17 5.1 考量行走時間的醫療健檢排程問題定義 17 5.1.1 問題描述 17 5.1.2 限制條件 18 5.1.3 數學模式 21 5.1.4 目標函式 26 5.2 醫療健檢排程問題之求解模式 30 5.2.1 求解架構 30 5.2.2 最早可排檢塊優先的啟發式排程演算程序 32 5.2.3 遺傳演算法 37 5.3 醫療健檢排程問題之動態排程演算模式 47 5.3.1 系統假設 47 5.3.2 動態排程演算程序 49 5.4 小結 56 第六章 求解效能分析 57 6.1 標竿問題 57 6.1.1 標竿問題格式 57 6.1.2 當日營運資訊的標竿問題 61 6.2 求解系統 64 6.3 範例測試及效能分析 76 6.3.1 靜態模式測試和效能分析 76 6.3.2 動態模式情境模擬及分析 83 6.4 小結 87 第七章 結論與未來研究建議 89 7.1 結論 89 7.2 未來研究建議 90 參考文獻 91 附錄A 94 附錄B 96 附錄C 97 附錄D 98 附錄E 99 附錄F 100 附錄G 102 附錄H 104 附錄I 105 附錄J 106 附錄K 107 附錄L 108 附錄M 109 附錄N 110 附錄O 111 附錄P 112 附錄Q 113 附錄R 114 | |
dc.language.iso | zh-TW | |
dc.title | 以基因演算法求解動態醫療健檢排程問題 | zh_TW |
dc.title | Heuristic Methods And Genetic Algorithms For Health Inspection Scheduling Problems | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 王宏鍇(Hung-Kai Wang),楊曙榮(Shu-Jung Yang),羅士哲(Shih-Che Lo) | |
dc.subject.keyword | 動態醫療健檢排程問題,重排程,啟發式排程演算程序,遺傳演算法,離散事件模擬, | zh_TW |
dc.subject.keyword | Dynamic Health Inspection Scheduling Problem,Rescheduling,Heuristic Scheduling Algorithm,Genetic Algorithm,Discrete Event Simulation, | en |
dc.relation.page | 115 | |
dc.identifier.doi | 10.6342/NTU202002548 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-06 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
顯示於系所單位: | 工業工程學研究所 |
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