A harmony search algorithm for nurse rostering problems

Harmony search algorithm (HSA) is a relatively new nature-inspired algorithm. It evolves solutions in the problem search space by mimicking the musical improvisation process in seeking agreeable harmony measured by aesthetic standards. The nurse rostering problem (NRP) is a well-known NP-hard scheduling problem that aims at allocating the required workload to the available staff nurses at healthcare organizations to meet the operational requirements and a range of preferences. This work investigates research issues of the parameter settings in HSA and application of HSA to effectively solve complex NRPs. Due to the well-known fact that most NRPs algorithms are highly problem (or even instance) dependent, the performance of our proposed HSA is evaluated on two sets of very different nurse rostering problems. The first set represents a real world dataset obtained from a large hospital in Malaysia. Experimental results show that our proposed HSA produces better quality rosters for all considered instances than a genetic algorithm (implemented herein). The second is a set of well-known benchmark NRPs which are widely used by researchers in the literature. The proposed HSA obtains good results (and new lower bound for a few instances) when compared to the current state of the art of meta-heuristic algorithms in recent literature

Applied partial constraint satisfaction using weighted iterative repair

Abstract. Many real-world constraint satisfaction problems (CSPs) can be over-constrained or too large to solve using a standard constructive/backtracking approach. Instead, faster heuristic techniques have been proposed that perform a partial search of all possible solutions using an iterative repair or hill-climbing approach. The main problem with such approaches is that they can become stuck in local minima. Consequently, various strategies or meta-heuristics have been developed to escape from local minima. This paper investigates the application of one such meta-heuristic, weighted iterative repair, to solving a real-world problem of scheduling nurses at an Australian hospital. Weighted iterative repair has already proved successful in solving various binary CSPs. The current research extends this work by looking at a non-binary problem formulation, and partial constraint satisfaction involving hard and soft constraints. This has lead to the development of a soft constraint heuristic to improve the level of soft constraint optimisation and an extension of the original weighted iterative repair that avoids certain forms of cyclic behaviour. It is also demonstrated that weighted iterative repair can learn from repeatedly solving the same problem. and that restarting the algorithm on the same problem can result in faster execution times. The overall results show that weighted iterative repair finds better quality solutions than a standard iterative repair, whilst approaching near optimal solutions in less time than an alternative integer programming approach.