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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 高成炎 | |
| dc.contributor.author | Chi-Way Wang | en |
| dc.contributor.author | 王麒瑋 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:15:25Z | - |
| dc.date.available | 2012-07-26 | |
| dc.date.copyright | 2007-07-26 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-19 | |
| dc.identifier.citation | [1]ACGME (Accreditation Council for Graduate Medical Education). Report of the Work Group on Resident Duty Hours and the Learning Environment, June 11, 2002.
[2]Aickelin, U. and Kathryn A. Dowsland. Exploiting problem structure in a genetic algorithm approach to a nurse. Journal of Scheduling, 3 (3), pp. 139-153, 2000. [3]Aickelin, U. and Paul White. Building Better Nurse Scheduling Algorithms. Annals of Operations Research 128, pp. 159–177, 2004. [4]Aickelin, U., E. K. Burke, and J. Li. An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering. Journal of Operational Research Society, pp. 1-12, 2006. [5]Bard, J. F. and Hadi W. Purnomo. Preference scheduling for nurses using column generation, European Journal of Operational Research 164, pp. 510–534, 2005. [6]Beaulieu, H., Jacques A. Ferland, Bernard Gendron and Philippe Michelon. A mathematical programming approach for scheduling physicians in the emergency room. Health Care Management Science, pp. 193–200, 2000. [7]Borndorfer, R., M. Grotschel and A. Lobel. Scheduling Duties by Adaptive Column Generation. Technical Report ZIB-report 01-02, Konrad-Zuze-Zentrum für Informationstechnik, Berlin, 2001. [8]Burke, E. K., David Elliman and Rupert Weare. A Genetic Algorithm Based University Timetabling System. East-West Conference on Computer Technologies in Education, pp. 35-40, 1994. [9]Burke, E. K., Patrick De Causmaecker, Sanja Petrovic and Greet Vanden Berghe. Fitness Evaluation for Nurse Scheduling Problems. Evolutionary Computation, vol. 2, pp. 1139-1146, 2001. [10]Burke, E. K., Patrick De Causmaecker, Sanja Petrovic, and G.Vanden Berghe. Metaheuristics for Handling Time Interval Coverage Constraints in Nurse Scheduling. Applied Artificial Intelligence, pp. 743-766, 2006. [11]Carter, Michael W. and Sophie D. Lapierre. Scheduling Emergency Room Physicians. Health Care Management Science, Vol. 4, No. 4, pp. 347-360, 2001. [12]Chen, kuan-Yu. Integrating Genetic Algorithms and Support Vector Regressionfor TAIEX Forecasting. Journal of Quantitative Management, Vol. 3, no. 1, pp 1-18, 2006. [13]Chen, Z. L. and W. B. Powell. Solving parallel machine scheduling problems by column generation. INFORMS, Journal on Computing, 1999. [14]Costa, D., An Evolutionary Tabu Search Algorithmand the NHL Scheduling Problem. INFOR, Vol. 33, No.3, pp. 161-178, 1995. [15]Davis, L. Handbook of genetic algorithms. Van Nostrand Reinhold, 1991. [16]Dowsland, K. A. Nurse scheduling with tabu search and strategic oscillation. European Journal of Operational Research, Vol. 106, Issues 2-3, pp. 393-407, 1998. [17]Gen, M., Tsujimura, Y. and Kubota, E. Solving job-shop scheduling problems by genetic algorithm. Systems, Man, and Cybernetics, 1994. 'Humans, Information and Technology'., 1994 IEEE International Conference, Vol. 2, pp. 1577-1582, 1994. [18]Gendreau, M., Jacques Ferland, Bernard Gendron, Noureddine Hail, Brigitte Jaumard, Sophie Lapierre, Gilles Pesant, and Patrick Soriano. Physician Scheduling in Emergency Rooms. The International Series of Conferences on the Practice and Theory of Automated Timetabling, pp. 2-14, 2006. [19]Goldberg, D. E. Genetic Algorithms in Search. Optimization, and Machine Learning. Addison Wesley, 1989. [20]Gomez, A., David de la Fuente, Javier Puente and Jose Parreno. A case-study about shift work management at a hospital emergency department with genetic algorithms. GECCO, pp. 1867-1868, 2006. [21]Gotteland, J.-B. and Durand, N. Genetic algorithms applied to airport ground traffic optimization. Evolutionary Computation, 2003. CEC '03. 544- 551 Vol.1, 2003. [22]Hui, Wei and Xu, Qing-xin. A knowledge-based creation of mathematical programming for GIS problem solving. Geoscience and Remote Sensing Symposium, IGARSS '05, Vol. 2, pp.936-939, 2005. [23]Inoue, T. and Takeshi Furuhashi, Member, IEEE, Hiroshi Maeda, and Minoru Takaba. A Proposal of Combined Method of Evolutionary Algorithm and Heuristics for Nurse Scheduling Support System. IEEE Transactions on Industrial Electronics, Vol. 50, pp. 196-203, 2003. [24]Jan, A., Masahito Yamamoto and Azuma Ohuchi. Evolutionary Algorithm for Nurse Scheduling Problem. Evolution Computer, pp. 196-203, 2000. [25]Kawanaka, H., Tomohiro Yoshikawa, Tsuyoshi Shinogi and Shinji Tsuruoka. Constraints and Search Efficiency in Nurse Scheduling Problem. Computational Intelligence in Robotics and Automation, pp. 312 – 317, 2003. [26]Langdon, W. B. Genetic programming and data structures : Genetic Programming + Data Structures = Automatic Programming!. The University of Birmingham. Kluwer Academic Publishers, 1998. [27]Li, Jingpeng and Aickelin, U. A Bayesian optimization algorithm for the nurse scheduling problem. Evolutionary Computation, Vol.3, pp.2149- 2156, 2003. [28]Li, W. F., Baowen Xu, Hongji Yang, William Cheng-Chung Chu and Chih-Wei Lu. Application of Genetic Algorithm in Search Engine. International Conference on Microelectronic Systems Education, pp. 366-371, 2000. [29]Lin, M.-L. and S.-S. Chou. Efficiency Assessment on Implementing Self-scheduling in an Intensive Care Unit. The Journal of Nursing, Vol. 46, pp.29-38, 1999. [30]Miller, H. E., William P. Pierskalla and Gustave J. Rath. Nurse Scheduling Using Mathematical Programming. Operations Research, Vol. 24, No. 5, Special Issue on Health Care, pp. 857-870, 1976. [31]Musliu, N., Schaerf A., and Slany W., Local search for shift design. European Journal of Operational Research 153, pp. 51-64, 2002. [32]Okada, M. and Masahiko Okada. A New Approach to the Nurse Scheduling Problem. Engineering in Medicine and Biology Society. Proceedings of the Annual International Conference of the IEEE, vol.3, pp. 1446 – 1447, 1988. [33]Pezzella, F. and Merelli, E. A tabu search method guided by shifting bottleneck for the job-shop scheduling problem. European Journal of Operational Research 120, pp. 297–310, 2000. [34]Sigl, B., Golub, M. and Mornar, V. Solving timetable scheduling problem using genetic algorithms. Information Technology Interfaces, pp.519 – 524, 2003. [35]Sun, L. M., Shr-Ming Juang, Jin-Jang Guo, Wen-Chi Wu, Geng-Shian Liou and Ming-Ting Chen. Scheduling Physician's Duties Automatically with the Computer. Chinese Journal of Plastic Surgery, Vol. 3, pp. 177-182, 2003. [36]Sun, L. M. Using A Modified Genetic Algorithm to Solve the Scheduling of Physician’s Shifts. Master. Thesis, University of Taipei Medical, Taipei, Taiwan, June 2005. [37]Vilcot, G., Billaut, J.-C. and Esswein, C. A Genetic Algorithm For A Bicriteria Flexible Job Shop Scheduling Problem. Service Systems and Service Management, Vol. 2, pp. 1240 – 1244, 2006. [38]Wang, Y. Z. Using genetic algorithm methods to solve course scheduling problems. Expert Systems with Applications 25, pp. 39–50, 2003. [39]Warner, D. M. Scheduling Nursing Personnel according to Nursing Preference: A Mathematical Programming Approach. Operations Research, Vol. 24, No. 5, Special Issue on Health Care, pp. 842-856, 1976. [40]Weil, G., K. Heus, Patrice Francois and Marc Poujode. Constraint Programming for Nurse Scheduling. IEEE Engineering in Medicine and Biology, Vol.14, No.4, pp. 417-422, 1995. [41]White, Christine A. and George M. White. Scheduling Doctors for Clinical Training Unit Rounds Using Tabu Optimization. PATAT 2002, pp. 120–128, 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29701 | - |
| dc.description.abstract | 本文主要描述一個在醫院重要的排班－住院醫師排班問題。論文中首先指出住院醫師排班問題主要著重如何在滿足三個關鍵且重要的需求－公平的分配工作、醫師選擇値班或不值班的計劃達成以及連續工作次數的避免(包含上月工作情況)，排出一個合理且令所有人滿意的班表。為了達成上述目的，本文採用基因演算法，求解，得此班表。不僅如此，針對上述住院醫師排班問題所需要達成的需求，改良傳統基因演算法的步驟－突變，命名為動態突變。本文相關實驗指出此一改良，可使演算法本身在搜尋最佳解的過程效能更好。 | zh_TW |
| dc.description.abstract | This thesis formally presents the resident physician scheduling problem, which is one of the most important scheduling problems in hospital. The resident physician scheduling problem is characterized as satisfying the fair schedule constraint, the physician specification constraint and the safe schedule constraint simultaneously. To minimize the penalties from violating the constraints, this study adopts the evolutionary approach to propose a genetic algorithm for solving the problems. In addition the well-known genetic operators, this study proposed a new mutation operator called dynamic mutation for solving the resident physician scheduling problem. The experimental results show that the proposed algorithm performs well in searching optimal schedules. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:15:25Z (GMT). No. of bitstreams: 1 ntu-96-R94922092-1.pdf: 3950376 bytes, checksum: a4b43accd66c58056cf1d2db348f5aef (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 謝辭 i
摘要 ii Abstract iii List of Figures vi List of Tables vii List of Algorithms vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Motivations 2 1.3 Contribution 3 1.4 Thesis Structure 4 Chapter 2 Related Work 5 2.1 Hospital Scheduling 5 2.2 Methods for hospital Scheduling 7 2.2.1. Manual Schedule Method 7 2.2.2. Goal programming Methods 7 2.2.2.1. Mathematical programming 7 2.2.2.2. Column generation 8 2.2.3. Meta-heuristics Methods 9 2.2.3.1. Tabu Search 9 2.2.3.2. Genetic Algorithm 10 Chapter 3 Problem Definition 12 3.1 The Problem Descriptions 12 3.2 The Notations 16 3.3 Solution Format and Constraints 17 3.4 Resident Physician Scheduling Problem 19 Chapter 4 Proposed Mechanism and Algorithm 22 4.1 The Chromosome and Initial Population 24 4.2 The Fitness Function 25 4.3 The Reproduction Methods 26 4.4 The Crossover Operators 28 4.5 The Mutation Operators 31 Chapter 5 Experimental Results and Comparisons 33 5.1. The Benchmark Problems 33 5.2. The Experimental Results and Comparisons 36 5.3. Algorithm Modified 42 5.4. The Improved Results 43 Chapter 6 Conclusions and Future Works 47 6.1 Conclusions 47 6.2 Future Works 48 Reference 49 Appendix 1: A roster of intern physicians in northern Taiwan 54 Appendix 2: A roster of emergency physicians in southern Taiwan 55 Appendix 3: A hospital roster rules of resident physicians in central Taiwan 56 | |
| dc.language.iso | en | |
| dc.subject | 啟發式演算法 | zh_TW |
| dc.subject | 基因演算法 | zh_TW |
| dc.subject | 住院醫師排班問題 | zh_TW |
| dc.subject | Meta-heuristic | en |
| dc.subject | Resident Physician Scheduling Problem | en |
| dc.subject | Genetic Algorithm | en |
| dc.title | 利用基因演算法解決住院醫師排班問題最佳化之研究 | zh_TW |
| dc.title | A Genetic Algorithm for Optimizing Resident Physician Scheduling Problem | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪炯宗,朱學亭,張春梵 | |
| dc.subject.keyword | 住院醫師排班問題,基因演算法,啟發式演算法, | zh_TW |
| dc.subject.keyword | Genetic Algorithm,Resident Physician Scheduling Problem,Meta-heuristic, | en |
| dc.relation.page | 58 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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