Abstract
Based on experiences with the ORBIS Dienstplan system [12] — a nurse rostering system that is currently used in about 60 German hospitals — this paper describes how to use constraint processing for automatic rostering. In practice, nurse rostering problems have many varying parameters: working time accounts, demands on crew attendance, set of used shifts, working time models, etc. Hence, rostering requires a flexible formalism for representing the variants of the problem as well as a robust search procedure that is able to cope with all problem instances. The described approach differs in mainly two points from other constraint-based approaches [1], [22] to rostering.
On the one hand, the used constraint formalism allows the integration of fine-grained optimization tasks by fuzzy constraints, which a roster may partially satisfy and partially violate. Such constraints have been used to optimize the amount of working time and the presence on the ward. In contrast, traditional frameworks for constraint processing consider only crisp constraints which are either completely violated or satisfied. On the other hand, the described system uses an any-time algorithm to search for good rosters. The traditional constraint-based approach for solving optimization tasks is to use extensions of the branch&bound. Unfortunately, performance of tree search algorithms is very sensitive to even minor changes in the problem representation. Therefore, ORBIS Dienstplan integrates the branch&bound into local search. The branch&;bound is used to enable the optimization of more than one variable assignment within one improvement step. This search algorithm converges quickly on good rosters and, additionally, enables a more natural integration of user interaction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abdennadher, S., Schlenker, H.: INTERDIP-an Interactive Constraint Based Nurse Scheduler. In: Proc. Int. Conf. on Practical Applications of Constraint Technology and Logic Programming. Practical Applications Expo, London (1999)
Bacchus, F., van Run, P.: Dynamic Variable Ordering in CSPs. In: Montanari, U., Rossi, F. (eds.): Proc. 1st Int. Conf. on Principles and Practice of Constraint Programming. Springer-Verlag, Berlin Heidelberg New York (1995) 258–275
Bistarelli, S., Fargier, H., Montanari, U., Rossi, F., Schiex, T., Verfaillie, G.: Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparisons. In: Jampel, M. (ed.): Over-Constrained Systems. Springer-Verlag, Berlin Heidelberg NewYork (1996)
Borning, A., Freeman-Benson, B., Wilson, M.: Constraint Hierarchies. Lisp and Symbolic Comput. 5 (1992) 233–270
Darmoni, S.J., Fajner, A., Leforestier, A., Mahé, N.: Horoplan: Computer-Assisted Nurse Scheduling Using Constraint-Based Programming. J. Soc. for Health Systems 5 (1995) 41–54
Dincbas, M., Van Hentenryck, P., Simonis, H., Aggoun, A., Graf, T., Berthier, F.: The Constraint Logic Programming Language CHIP. In: Proc. Int. Conf. on Fifth Generation Computer Systems (1988)
Dubois, D., Fargier, H., Prade, H.: The Calculus of Fuzzy Restrictions as a Basis for Flexible Constraint Satisfaction. In: Proc. 2nd IEEE Int. Conf. on Fuzzy Systems (San Francisco, CA) (1993) 1131–1136
Fages, F., Fowler, J., Sola, T.: Handling Preferences in Constraint Logic Programming with Relational Optimization. In: Jampel, M. (ed.): Over-Constrained Systems. Springer-Verlag, Berlin Heidelberg New York (1996)
Freuder, E.C., Wallace, R.J.: Partial Constraint Satisfaction. Artif. Intell. 58 (1992) 21–70
Gaschnig, J.: A General Backtrack Algorithm that Eliminates Most Redundant Checks. In: Proc. IJCAI-77 (Cambridge, MA) (1977)
Meyer auf’m Hofe, H.: Partial Satisfaction of Constraint Hierarchies in Reactive and Interactive Configuration. In: Hower, W. Ruttkay, Z. (eds.): ECAI-96Workshop on Non-Standard Constraint Processing (1996) 61–72
Meyer auf’m Hofe, H.: ConPlan/SIEDAplan: Personnel Assignment as a Problem of Hierarchical Constraint Satisfaction. In: PACT-97: Proc. 3rd Int. Conf. on the Practical Application of Constraint Technology (Practical Application Expo, London) (1997) 257–272
Meyer auf’m Hofe, H.: Kombinatorische Optimierung mit Constraintverfahren — Problemlösung ohne anwendungsspezifische Suchstrategien. DISKI — Dissertationen zur Künstlichen Intelligenz, Vol. 242. Infix, Akademische Verlagsgesellschaft (2000) ISBN 3-89838-242-7
Meyer auf’m Hofe, H., Abecker, A.: ConPlan: Solving Scheduling Problems Represented by Soft Constraints. In: ISIAC-98:Proc. Int. Symp. on Intelligent Automation and Control (Anchorage, AK) (1998) 245–250
Nilsson, N.: Principles of Artificial Intelligence. In: Symbolic Computation. Springer-Verlag, Berlin Heidelberg New York (1982) chapter 3.2
Schiex, T., Fargier, H., Verfaillie, G.: Valued Constraint Satisfaction Problems: Hard and Easy Problems. In: Mellish, C. (ed.): IJCAI-95: Proc. 14th Int. Joint Conf. on Artif. Intell. Kaufmann, San Francisco, CA (1995) 631–637
Snow, P., Freuder, E.C.: Improved Relaxation and Search Methods forApproximate Constraint Satisfaction with a Maximin Criterion. In: Proc. 8th Biennial Conf. of the Can. Soc for Comput. Studies of Intelligence (1990) 227–230
Steinmann, O., Strohmeier, A., Stützle, T.: Tabu Search vs. Random Walk. In: Brewka, G., Habel, C., Nebel, B. (eds.): KI-97: Advances in Artificial Intelligence. LNAI, Vol. 1303. Springer-Verlag, Berlin Heidelberg NewYork (1997) 337–348
Tsang, E.: Foundations of Constraint Satisfaction. Computation in Cognitive Science. Academic, London (1993) ISBN 0-12-701610-4
Wallace, M., Novello, S., Schimpf, J.: Eclipse-a Platform for Constraint Logic Programming. ICL Systems J. 12 (1997) 159–200
Wallace, R.J.: Analysis of Heuristic Methods for Partial Constraint Satisfaction Problems. In: CP-96: Proc. 2nd Int. Conf. on Principles and Practice of Constraint Processing. Springer-Verlag, Berlin Heidelberg NewYork (1996) 482–496
Weil, G., Heus, K.: Eliminating Interchangeable Values in the Nurse Scheduling Problem Formulated as a Constraint Satisfaction Problem. In: CONSTRAINT-95: The FLAIRS-95 Int.Workshop on Constraint-Based Reasoning (Melbourne Beach, FL) (1995)
Zadeh, L.A.: Fuzzy Sets. Information and Control 8 (1965) 338–353
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Meyer auf’m Hofe, H. (2001). Solving Rostering Tasks as Constraint Optimization. In: Burke, E., Erben, W. (eds) Practice and Theory of Automated Timetabling III. PATAT 2000. Lecture Notes in Computer Science, vol 2079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44629-X_12
Download citation
DOI: https://doi.org/10.1007/3-540-44629-X_12
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42421-5
Online ISBN: 978-3-540-44629-3
eBook Packages: Springer Book Archive