Flexible Shift Planning in the Service Industry

The Case of Physicians in Hospitals
1. Auflage 2010
ISBN: 978-3-642-10517-3
Verlag: Springer
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 117 Seiten

The introductory chapter consists of four sections. In Sect. 1. 1 we reveal the c- rent situation in hospitals that is faced by the management. We address the general issue of personnel scheduling in the service industry in Sect. 1. 2. Then we motivate our research by considering physicians as the scheduling object. In particular, we show the complex nature of physician scheduling in a hospital environment. The focus of the research is presented in Sect. 1. 3. Finally, we conclude the chapter by illustrating the outline of the thesis. 1. 1 General Economic Situation in Hospitals The mounting pressure in the health care industry to reduce costs is forcing hos- tals and related facilities to take a closer look at their staf?ng policies (see [111]). A primary dif?culty in reducing personnel costs, the major component of the budget, is the variability in demand and the need to assign staff to ?xed shifts. Furthermore, government run facilities, especially those in the European Union, are seeing their budgets cut in terms of real dollars despite an aging and more acutely ill patient population (e. g. , see [96]). It has been reported that up to a third of the hospitals in Germanyplan a reductionin staff (see [91]). The schedulingprocess is furtherc- plicated by the generally recognized importance of taking individual preferences intoaccount. Moreattractiveschedulespromotejobsatisfaction,increaseproduct- ity,and reduceturnover(cf. [2]). However,withoutimprovedschedulingprocedures that better match supplyto demand,the level of care that theynow providewill soon become unsustainable. 1.

Autoren/Hrsg.

Inhaltsverzeichnis

1;Contents;6
2;List of Figures;8
3;1 Introduction;10
3.1;1.1 General Economic Situation in Hospitals;10
3.2;1.2 Complexity of Physician Scheduling;10
3.3;1.3 Topic of This Research;11
3.4;1.4 Outline;12
4;2 Literature Review on Personnel Scheduling;14
4.1;2.1 General Personnel Scheduling;14
4.2;2.2 Physician Scheduling;17
4.3;2.3 Implicit Shift Modeling;18
4.4;2.4 Column Generation and B&P;19
5;3 MIP Model for Flexible Shift Scheduling of Physicians;22
5.1;3.1 Basic MIP Model;22
5.1.1;3.1.1 Model Description;22
5.1.1.1;3.1.1.1 Hard Constraints;23
5.1.1.2;3.1.1.2 Soft Constraints;23
5.1.2;3.1.2 Model Formulation;26
5.2;3.2 Model Enhancements;32
5.2.1;3.2.1 On-Call Services;32
5.2.2;3.2.2 Time Window Restrictions;36
5.2.3;3.2.3 Break Assignment;37
5.2.3.1;3.2.3.1 Break Placement After a Predefined Time Span;37
5.2.3.2;3.2.3.2 Implicit Break Placement in a Defined Time Interval;38
5.2.3.3;3.2.3.3 Break Placements When Short Shifts are Considered;40
5.2.4;3.2.4 Holidays and Vacations;41
5.3;3.3 Case Study: Anesthetist Scheduling;43
5.3.1;3.3.1 Current Practice;43
5.3.2;3.3.2 Solution of the Model;44
6;4 Solution Methodologies;47
6.1;4.1 Preprocessing;47
6.2;4.2 Heuristic Decomposition Strategy;49
6.3;4.3 Column Generation and B&P Algorithm;52
6.3.1;4.3.1 Master Problem Formulation;54
6.3.2;4.3.2 Subproblem Formulation;57
6.3.3;4.3.3 Finding Integer Solutions;62
6.3.4;4.3.4 Branching on MP Variables (MPVarB);63
6.3.5;4.3.5 Branching on SP Variables (SPVarB);66
6.3.6;4.3.6 A Dual Point of View;69
6.3.6.1;4.3.6.1 Dual Space by MPVarB;69
6.3.6.2;4.3.6.2 Dual Space by SPVarB;70
6.3.7;4.3.7 Heuristics for the B&P Algorithm;71
6.3.7.1;4.3.7.1 Initialization Heuristic;71
6.3.7.2;4.3.7.2 Feasibility Heuristic to Find Integer Solutions;72
6.3.7.3;4.3.7.3 Rounding Heuristic to Find Integer Solutions;74
6.3.8;4.3.8 Enhancements for the B&P Algorithm;75
6.3.8.1;4.3.8.1 Lower Bounds and Early Termination;75
6.3.8.2;4.3.8.2 Aggregation of Subproblems;78
7;5 Experimental Investigations;81
7.1;5.1 Input Data From MRI;81
7.1.1;5.1.1 Demand Profiles;82
7.1.2;5.1.2 Basic Parameter Settings;87
7.2;5.2 Heuristic Decomposition;88
7.2.1;5.2.1 Analysis of Different Model Features;90
7.2.2;5.2.2 Parametric Analysis;92
7.2.2.1;5.2.2.1 Maximum Shift Length;92
7.2.2.2;5.2.2.2 Time Window Length;93
7.2.2.3;5.2.2.3 Number of Physicians;93
7.2.3;5.2.3 Analysis of Instances of Different Sites;94
7.3;5.3 B&P Algorithm;95
7.3.1;5.3.1 Two-Week Problems;100
7.3.2;5.3.2 Four-Week Problems;101
7.3.3;5.3.3 Six-Week Problems;101
7.3.4;5.3.4 General Observations;102
7.4;5.4 Comparison of Both Algorithms;102
8;6 Conclusions and Further Remarks;106
8.1;6.1 Summary and Conclusions;106
8.2;6.2 Final Remarks and Further Research Directions;108
9;Appendix;109
9.1;A.1 Abbreviations, Notation, and Symbols;109
10;Bibliography;116