The Doctor Rostering Problem - Asser Fahrenholz

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Chapter 2. The Doctor Rostering Problem 7 original algorithms. Burke et al. [3] applies the model to several hospitals in Belgium, taking a dynamic, user-defined, set of constraints into account. Burke et al. [5] ends their review by concluding that very few of the solutions are applicable to real world problems and highlights a range of areas they believe would benefit from more research. These includes Robustness (if a person calls in sick, most solutions suffer a chain-reaction of disruptions) and Ease of Use (no hospital administrator is able to set the parameters for any algorithm designed to solve a problem), noting how more research is needed on parameter-less algorithms. The varying nature of the problems solved in the literature, including the size of the staff, the complexity and amount of constraints, the type of shifts, demands, preferences and qualifications to name a few, indicates that NSP can not be classified as only one problem. For this to be the case, a uniform model of the problem is needed.
Chapter 3 A mathematical model for the DRP This chapter describes the mathematical model for the doctor rostering problem stated by the medical practice. A number of constraints was given; some which has to be satisfied (called hard constraints), and some which are not essential, but are preferably and as much as possible satisfied (the soft constraints). The hard constraints also partitions the solution space into feasible and infeasible. A solution is said to be feasible when no hard constraints are violated and infeasible otherwise. In this project, I model the DRP as a minimisation problem. 3.1 Parameters The constraints have all been thoroughly discussed with the medical practice throughout the project. It should be noted that these all stem from personal point-of-views, wishes etc. as the medical practice is a private one, and not a public institution, which would no doubt be heavily influenced by union-restrictions and/or laws. In describing the constraints, the following binary variable is used: xijk = 1 if on day i, shift j, the doctor is k 0 otherwise And the following parameters are introduced: 8
Page 1 and 2: TECHNICAL UNIVERSITY OF DENMARK The
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Page 5 and 6: Acknowledgements Foremost, I would
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Chapter 9. Conclusion 58 The three
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Appendix B GAMS Model This chapter
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Appendix C. Tests 88 C.1.1 Instance
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Appendix C. Tests 94 JST PZH HP SDA
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Appendix C. Tests 102 Test 3 RCL 1
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Bibliography 118 [10] Michael R. Ga
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