Computers and Operations Research
Tabu Search
A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
The State of the Art of Nurse Rostering
Journal of Scheduling
New Multiobjective Metaheuristic Solution Procedures for Capital Investment Planning
Journal of Heuristics
An electromagnetic meta-heuristic for the nurse scheduling problem
Journal of Heuristics
A decision support system for cyclic master surgery scheduling with multiple objectives
Journal of Scheduling
A grasp-knapsack hybrid for a nurse-scheduling problem
Journal of Heuristics
INFORMS Journal on Computing
Benchmarking a wide spectrum of metaheuristic techniques for the radio network design problem
IEEE Transactions on Evolutionary Computation
Memes, self-generation and nurse rostering
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A shift sequence based approach for nurse scheduling and a new benchmark dataset
Journal of Heuristics
An evolutionary approach for the nurse rerostering problem
Computers and Operations Research
Memetic algorithms for nurse rostering
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Hybrid swarm-based optimization algorithm of GA & VNS for nurse scheduling problem
ICICA'11 Proceedings of the Second international conference on Information Computing and Applications
A Time Predefined Variable Depth Search for Nurse Rostering
INFORMS Journal on Computing
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Nurse rostering problems consist of assigning varying tasks, represented as shift types, to hospital personnel with different skills and work regulations. The goal is to satisfy as many soft constraints and personal preferences as possible while constructing a schedule which meets the required personnel coverage of the hospital over a predefined planning period. Real-world situations are often so constrained that finding a good quality solution requires advanced heuristics to keep the calculation time down. The nurse rostering search algorithms discussed in this paper are not aimed at specific hospitals. On the contrary, the intention is that such algorithms should be applicable across the whole sector. Escaping from local optima can be very hard for the metaheuristics because of the broad variety of constraints. In this paper, we present a variable neighborhood search approach. Hidden parts of the solution space become accessible by applying appropriate problem specific neighborhoods. The method allows for a better exploration of the search space, by combining shortsighted neighborhoods, and very greedy ones. Experiments demonstrate how heuristics and neighborhoods can be assembled for finding good quality schedules within a short amount of calculation time.