and previously written overviews, we present comparisons for measures that determine the complexity of nurse rostering problems. We describe and discuss solution approaches which span the interdisciplinary spectrum from operations research techniques to artificial intelligence methods. We conclude by drawing

Hyperheuristics can be defined to be heuristics which choose between heuristics in order to solve a given optimisation problem. The main motivation behind the development of such approaches is the goal of developing automated scheduling methods which are not restricted to one problem. In this paper

nurse rostering problem is introduced. Memetic Algorithms utilizing different type of promising genetic operators and a self adaptive violation directed hierarchical hill climbing method are presented based on a previously proposed framework.

This paper considers the problem of building cyclic staff schedules (rosters). These rosters are found in a wide variety of industries, includ- ing ambulance operations, airport operations and hospitals. We discuss a number of integer prograrrmaing models that have been developed to solve more

In this paper we introduce the extended linear numberings method for evaluating the quality of employees ’ rosters. The extension to the original linear numberings method was necessary to cover a wider range of constraints from various employee rostering problems. We created an XML format

Many rostering methods first create shifts from some given staffing levels, and after that create rosters from the set of created shifts. Although such a method has some nice properties, it also has some bad ones. In this paper we outline a method that creates rosters directly from staffing

Nurse rostering is a difficult search problem with many constraints. In the literature, a number of approaches have been investigated including penalty function methods to tackle these constraints within genetic algorithm frameworks. In this paper, we investigate an extension of a previously

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

this paper, we present a variable neighbourhood search approach for the nurse rostering problem. The method allows for a better exploration of the search space, by combining short sighted neighbourhoods and very greedy ones

The benefits of automating the rostering process include reducing the planning workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and to attract more people into the profession. Better