Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Record breaking optimization results using the ruin and recreate principle
Journal of Computational Physics
A Memetic Approach to the Nurse Rostering Problem
Applied Intelligence
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
An indirect genetic algorithm for a nurse-scheduling problem
Computers and Operations Research
Variable neighborhood search for nurse rostering problems
Metaheuristics
The State of the Art of Nurse Rostering
Journal of Scheduling
Cyclic preference scheduling of nurses using a Lagrangian-based heuristic
Journal of Scheduling
An evolutionary squeaky wheel optimization approach to personnel scheduling
IEEE Transactions on Evolutionary Computation
Memetic algorithms for nurse rostering
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
A hybrid evolutionary approach to the nurse Rostering problem
IEEE Transactions on Evolutionary Computation
Iterated local search in nurse rostering problem
Proceedings of the Fourth Symposium on Information and Communication Technology
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Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e., the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and the natural mutation process on these components, respectively, to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated for them to remain there. This demonstration employs an evaluation function that evaluates how well each component contributes toward the final objective. Two elimination steps are then applied: the first elimination removes a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.