Simulated annealing: theory and applications
Simulated annealing: theory and applications
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
General Cooling Schedules for a Simulated Annealing Based Timetabling System
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
EasyLocal++: an object-oriented framework for the flexible design of local-search algorithms
Software—Practice & Experience
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
The State of the Art of Nurse Rostering
Journal of Scheduling
A hybrid tabu search algorithm for automatically assigning patients to beds
Artificial Intelligence in Medicine
INFORMS Journal on Computing
Modern Applied Statistics with S
Modern Applied Statistics with S
Modeling and solving the dynamic patient admission scheduling problem under uncertainty
Artificial Intelligence in Medicine
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We propose a multi-neighborhood local search procedure to solve a healthcare problem, known as the Patient Admission Scheduling problem. We design and experiment with different combinations of neighborhoods, showing that they have diverse effectiveness for different sets of weights of the cost components that constitute the objective function. We also compute many lower bounds based on the relaxation of some constraints. The outcome is that our results compare favorably with the previous work on the problem, improving all available instances, and in some cases are also quite close to the lower bounds. Finally, we propose the application of the technique to the dynamic case, in which admission and discharge dates are not predictable in advance.