Solving Nurse Rostering Problem Using Artificial Bee Colony Algorithm

Abstract

Abstract-Artificial bee colony algorithm(ABC) is proposed as a new nature-inspired algorithm which has been successfully utilized to tackle numerous class of optimization problems belongs to the category of swarm intelligence optimization algorithms. The major focus of this paper is to show that ABC could be used to generate good solutions when adapted to tackle the nurse rostering problem (NRP). In the proposed ABC for the NRP, the solution methods is divided into two phases. The first uses a heuristic ordering strategy to generate feasible solutions while the second phase employs the usage of ABC algorithm in which its operators are utilized to enhance the feasible solutions to their optimality. The proposed algorithm is tested on a set of 69 problem instances of the dataset introduced by the First International Nurse Rostering Competition 2010 (INRC2010). The results produced by the proposed algorithm are very promising when compared with some existing techniques that worked on the same dataset. Further investigation is still necessary for further improvement of the proposed algorithm.Keywords-Nurse Rostering; Artificial Bee Colony Algorithm; Swarm Intelligence Method; Nature-inspired algorithm I. INTRODUCTION The nurse rostering problem (NRP) is among the timetabling problem that is widely investigated by the researchers in the domain of operations research and artificial intelligence. The NRP as a NP-hard problem is described as an assignment of a set of qualified nurses to a different set of shifts over a predetermined scheduling period, subject to a set of hard and soft constraints. The hard constraints is the type that must be fulfilled for the roster to be feasible whereas the violations of soft constraints in the NRP are allowed but should be minimized as much as possible. It is noteworthy that the quality of the roster is determined by the satisfaction of the soft constraints in a feasible roster. The basic objective of NRP is to generate a feasible roster of high quality. However, studies in NRP domain have shown that it is almost impossible to find a roster that satisfies all constraints, since the NRP is classified as a combinatorial optimization problem This paper tackles the NRP dataset proposed by the First International Nurse Rostering Competition (INRC2010), which is organized by the CODeS research group at Katholieke Universiteit Leuven in Belgium, SINTEF Group in Norway and the University of Udine in Italy. The dataset of the INRC2010 is classified into three tracks: sprint, medium, and long datasets, which are varied in size and complexity. Each track is grouped into four categories in accordance with their publication time at the competition: early, late, hidden, and hint. Few techniques proposed to solve the INRC2010 dataset during and after the competition are review as follows. Valouxis et al. in [26] applied Integer Programming (IP) to tackle the NRP using INRC2010 dataset in which their solution method consists of two stages: the first stage consists of assigning different nurses to ...

Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

“…Several methods have been proposed using the INRC2010 dataset to solve the NRP; the authors have considered five latest competitors to measure the effectiveness of the proposed algorithm. Asaju et al [ 14 ] proposed Artificial Bee Colony (ABC) algorithm to solve NRP. This process is done in two phases; at first heuristic based ordering of shift pattern is used to generate the feasible solution.…”