New Reward-Based Movement to Improve Globally-Evolved BCO in Nurse Rostering Problem

Nurse Rostering Problem (NRP) is a crucial problem in hospital industry with combinatorial complex problem. NRP is one of the NP-Hard problems, which means that today there is no definite algorithm that is capable of solving the problem. In this paper, a metaheuristic approach called Reward-Based Movement for Bee Colony Optimization (RBMBCO) is proposed to solve the NRP. It is evaluated using an NRP instance of 30 nurses for 4 weeks of assignment from The Second International Nurse Rostering Competition (INRC-II) dataset. The experimental results show that RBMBCO is capable of generating a better solution than the standard Globally-Evolved Bee Colony Optimization.