The Doctor Rostering Problem - Asser Fahrenholz

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Bibliography [1] Emile Aarts, Jan Korst, and Wil Michiels. Search Methodologies - Introductory Tu- torials in Optimization and Decision Support Techniques, pages 187–210. Springer, 2005. [2] Jonathan F. Bard and Hadi W. Purnomo. Short-term nurse scheduling in response to daily fluctuations in supply and demand. Health Care Management Sciences, 8: 315–324, 2005. [3] Edmund Burke, Patrick De Causmaecker, and Greet Vanden Berghe. A Hybrid Tabu Search for the Nurse Rostering Problem, pages 187–194. Springer, 1999. Lecture Notes in Artificial Intelligence, vol. 1585. [4] Edmund K. Burke and Graham Kendall. Search Methodologies - Introductory Tu- torials in Optimization and Decision Support Techniques, pages 5–18. Springer, 2005. [5] Edmund K. Burke, Patrick De Causmaecker, Greet Vanden Berghe, and Hen- drik Van Landeghem. The state of the art of nurse rostering. Journal of Scheduling, 7:441–499, 2004. [6] Stephen Cook, editor. The Complexity of Theorem Proving Procedures, 1971. Pro- ceedings of the third annual ACM symposium on Theory of computing. [7] Tiago M. Dias, Daniel F. Ferber, Cid C. de Souza, and Arnaldo V. Moura. Con- structing nurse schedules at large hospitals. International Transactions In Opera- tional Research, 10:245–265, 2003. [8] Kathryn A. Dowsland. Nurse scheduling with tabu search and strategic oscillation. European Journal of Operational Research, 106(2):393–407, 1998. [9] S. Even, A. Itai, and A. Shamir. On the complexity of timetable and multicom- modity flow problems. SIAM J. Comput, 5:691–703, 1976. 117
Bibliography 118 [10] Michael R. Garey and David S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman & Co. New York, NY, USA, 1979. [11] F. Glover. Future paths for integer programming and links to artificial intelligence. Comput. Oper. Res., (13):533–549, 1986. [12] Fred Glover and Fred Laguna. Tabu Search. Kluwer Academic Publishers, 1997. [13] John E. Hopcroft, Rajeev Motwani, and Jeffrey D. Ullman. Introduction to Au- tomata Theory, Languages, and Computation. Addison-Wesley, 2001. [14] Rudy Hung. Improving productivity and quality through workforce scheduling. Industrial Management, 34(6):4–6, 1992. [15] Richard M. Karp. Complexity of Computer Computations, pages 85–103. R. E. Miller and J. W. Thatcher (editors). New York: Plenum, 1972. Chapter: Re- ducibility Among Combinatorial Problems. [16] Amnon Meisels and Andrea Schaerf. Solving employee timetabling problems by generalized local search. In Proceedings of the 6th Congress of the Italian Associa- tion for Artificial Intelligence on Advances in Artificial Intelligence, pages 380–389, 1999. [17] Henrik Berg Nielsen. Algoritmer til vagtplanlægning for sygeplejersker. Master’s thesis, Syddansk Universitet Odense, Juni 2006. [18] Koji Nonobe and Toshihide Ibaraki. An improved tabu search method for the weighted constraint satisfaction problem. INFOR, 39(1):131–151, 2001. [19] Mauricio G. C. Resende and Thomas A. Feo. Greedy randomized adaptive search procedures. Technical report, The University of Texas, Austin, TX 78712 USA and AT&T Bell Laboratories, Murray Hill, NJ 07974 UAS, February 1994. [20] Mauricio G. C. Resende and Celso C. Ribeiro. Greedy randomized adaptive search procedures. Technical Report TD-53RSJY, AT&T Labs Research, August 2002. Version 2. To appear in State of the Art Handbook in Metaheuristics, F. Glover and G. Kochenberger, eds., Kluwer, 2002. [21] Robert Sedgewick and Kevin Wayne. Perm.java, 2006–2007. URL http://www. cs.princeton.edu/introcs/23recursion/Perm.java.html. [22] Rhian Silvestro and Claudio Silvestro. An evaluation of nurse rostering practices in the national health service. Journal of Advanced Nursing, 32(3):525–535, 2000.
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TECHNICAL UNIVERSITY OF DENMARK The
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TECHNICAL UNIVERSITY OF DENMARK Abs
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Acknowledgements Foremost, I would
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Contents vi 4.3.1 A construction he
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List of Figures 4.1 The transformat
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List of Algorithms 4.1 Adaptive gre
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Chapter 1. Introduction and problem
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Chapter 2. The Doctor Rostering Pro
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Chapter 2. The Doctor Rostering Pro
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Chapter 3 A mathematical model for
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Chapter 3. The model and design 10
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Chapter 4. Solving the DRP 16 it in
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Chapter 4. Solving the DRP 18 4.3.1
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Chapter 4. Solving the DRP 20 Given
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Chapter 4. Solving the DRP 22 Since
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Chapter 4. Solving the DRP 24 1. Co
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Chapter 4. Solving the DRP 26 decre
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Chapter 4. Solving the DRP 28 This
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Chapter 4. Solving the DRP 30 Simul
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Chapter 5. Optimal solution 32 The
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Chapter 6 The DRP Program Through t
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Chapter 6. The DRP Program 36 Figur
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Chapter 6. The DRP Program 38 of th
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Chapter 6. The DRP Program 40 Figur
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Chapter 7 Metaheuristic tests This
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Chapter 7. Tests, results and discu
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Chapter 8. Future considerations 54
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Chapter 8. Future considerations 56
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Chapter 9. Conclusion 58 The three
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Appendix A. Implementation 60 27 //
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2 Appendix A. Implementation 62 3 i
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Appendix A. Implementation 64 A.3 G
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Page 87 and 88: Appendix B GAMS Model This chapter
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Page 99 and 100: Appendix C. Tests 88 C.1.1 Instance
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Page 113 and 114: Appendix C. Tests 102 Test 3 RCL 1
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