A Constraint-directed Local Search Approach to Nurse Rostering Problems

TL;DR

This paper explores hybrid constraint programming and local search methods with large neighbourhood search strategies to improve nurse rostering solutions, analyzing three fragment selection strategies for better problem-solving.

Contribution

It introduces and compares three novel strategies for selecting variable fragments in large neighbourhood search for nurse rostering, enhancing solution quality.

Findings

Three fragment selection strategies are analyzed and compared.

Using soft constraint violation information improves neighborhood selection.

Promising results suggest potential for hybrid approach enhancements.

Abstract

In this paper, we investigate the hybridization of constraint programming and local search techniques within a large neighbourhood search scheme for solving highly constrained nurse rostering problems. As identified by the research, a crucial part of the large neighbourhood search is the selection of the fragment (neighbourhood, i.e. the set of variables), to be relaxed and re-optimized iteratively. The success of the large neighbourhood search depends on the adequacy of this identified neighbourhood with regard to the problematic part of the solution assignment and the choice of the neighbourhood size. We investigate three strategies to choose the fragment of different sizes within the large neighbourhood search scheme. The first two strategies are tailored concerning the problem properties. The third strategy is more general, using the information of the cost from the soft constraint violations and their propagation as the indicator to choose the variables added into the fragment. The three strategies are analyzed and compared upon a benchmark nurse rostering problem. Promising results demonstrate the possibility of future work in the hybrid approach.