An Ant Colony Optimization Approach for Nurse Rostering Problem

Nurse rostering is a non-deterministic polynomial problem with many constraints. In the literature, a number of heuristic approaches have been proposed, but few of them can achieve satisfying performance on both solution quality and search speed. Inspired by the successful experience of ant colony optimization (ACO) on many highly-constrained problems, this paper proposed an ant colony optimization approach termed ACO-NR for solving the nurse rostering problem. First, the search space of the nurse rostering problem is remodeled as a graph, with each solution corresponding to a path on the graph. Then a heuristic function is designed to guide the path construction behavior of ACO-NR. The heuristic information comes not only from the static information defined by the problem-dependent knowledge, but also from the dynamic information generated by the solution construction procedure. A penalty function is defined to help ACO-NR handle problem constraints. Experimental results on 52 benchmark instances show that the proposed ACO-NR can achieve better performance than classic nurse rostering algorithms.