We present a hybrid approach of goal programming and meta-heuristic search to find compromise solutions for a difficult employee scheduling problem, i.e. nurse rostering with many hard and soft constraints. By employing a goal programming model with different parameter settings in its objective...

This paper presents a hybrid multi-objective model that combines integer programming (IP) and variable neighbourhood search (VNS) to deal with highly-constrained nurse rostering problems in modern hospital environments. An IP is first used to solve the subproblem which includes the full set of...

This paper presents a state transition based formal framework for a new search method, called Evolutionary Ruin and Stochastic Recreate, which tries to learn and adapt to the changing environments during the search process. It improves the performance of the original Ruin and Recreate principle...

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions...

Due to its complexity, its challenging features, and its practical relevance, personnel scheduling has been heavily investigated in the last few decades. However, there is a relatively low level of study on models and complexity in these important problems. In this paper, we present mathematical...

This paper presents the results of developing a branch and price algorithm and an ejection chain method for nurse rostering problems. The approach is general enough to be able to apply it to a wide range of benchmark nurse rostering instances. The majority of the instances are real world...