Memetic algorithms: a short introduction
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
CSPLIB: A Benchmark Library for Constraints
CP '99 Proceedings of the 5th International Conference on Principles and Practice of Constraint Programming
The State of the Art of Nurse Rostering
Journal of Scheduling
A hybrid setup for a hybrid scenario: combining heuristics for the home health care problem
Computers and Operations Research
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Handbook of Constraint Programming (Foundations of Artificial Intelligence)
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
A Hybrid Algorithm for Computing Tours in a Spare Parts Warehouse
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
A hybrid evolutionary approach to the nurse Rostering problem
IEEE Transactions on Evolutionary Computation
Hi-index | 0.00 |
We focus on hybrid solution methods for a large-scale real-world multimodal homecare scheduling (MHS) problem, where the objective is to find an optimal roster for nurses who travel in tours from patient to patient, using different modes of transport. In a first step, we generate a valid initial solution using Constraint Programming (CP). In a second step, we improve the solution using one of the following metaheuristic approaches: (1) variable neighborhood descent, (2) variable neighborhood search, (3) an evolutionary algorithm, (4) scatter search and (5) a simulated annealing hyper heuristic. Our evaluation, based on computational experiments, demonstrates how hybrid approaches are particularly strong in finding promising solutions for large real-world MHS problem instances.