Showing papers by "Rong Qu published in 2008"
TL;DR: In this paper, an intelligent decision support methodologies for nurse rostering problems in large modern hospital environments is presented. But the amount of computational time that is allowed plays a significant role and the authors analyse and discuss this.
172 citations
09 Dec 2008
TL;DR: This work investigates a two-stage hybrid CP approach using a constraint satisfaction model to generate weekly rosters consist of high quality shift sequences satisfying a subset of constraints and a simple Variable Neighborhood Search is used to improve the solution obtained.
Abstract: Due to the complexity of nurse rostering problems (NRPs), Constraint Programming (CP) approaches on their own have shown to be ineffective in solving these highly constrained problems. We investigate a two-stage hybrid CP approach on real world benchmark NRPs. In the first stage, a constraint satisfaction model is used to generate weekly rosters consist of high quality shift sequences satisfying a subset of constraints. An iterative forward search is then adapted to extend them to build complete feasible solutions. Variable and value selection heuristics are employed to improve the efficiency. In the second stage, a simple Variable Neighborhood Search is used to quickly improve the solution obtained. The basic idea of the hybrid approach is based on the observations that high quality nurse rosters consist of high quality shift sequences. By decomposing the problems into solvable sub-problems for CP, the search space of the original problems are significantly reduced. The results on benchmark problems demonstrate the efficiency of this hybrid CP approach when compared to the state-of-the-art approaches in the literature.
48 citations
TL;DR: In this article, a genetic algorithm was used to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production.
Abstract: Previous research has shown that artificial immune systems can be used to produce robust schedules in a manufacturing environment. The main goal is to develop building blocks (antibodies) of partial schedules that can be used to construct backup solutions (antigens) when disturbances occur during production. The building blocks are created based upon underpinning ideas from artificial immune systems and evolved using a genetic algorithm (Phase I). Each partial schedule (antibody) is assigned a fitness value and the best partial schedules are selected to be converted into complete schedules (antigens). We further investigate whether simulated annealing and the great deluge algorithm can improve the results when hybridised with our artificial immune system (Phase II). We use ten fixed solutions as our target and measure how well we cover these specific scenarios.