Applying Combinatorial Particle Swarm Optimization In Nurse Rostering Problem

Abstract

As the global pandemic named “coronavirus” brings the world to a collective gloom, each country’s national health service workers move heaven and earth, daily, to save lives. However, as the positive cases keeps skyrocketing, scenes of them being tired out by two-digit consecutive hours of work is not something uncommon. Hence, rostering nurses to serve people in the most optimized way should be a priority. This bachelor’s thesis attempts to contribute to that topic, specifically investigate the nurse rostering problem (NRP) - defined as the problem of assigning shifts to available employees over a planning period. The main objective of this thesis is to find and apply a new iterative algorithm for solving NRP. The chosen algorithm in this research is Particle Swarm Optimization Algorithm or PSO in short. This paper will use a new data that has the original benchmark instances at ‘Employee Shift Scheduling Benchmark Data Sets” [1], as input to the mathematical models. The mentioned algorithm’s performance will be compared with other heuristics to evaluate the effectiveness of the new method on a specific NRP dataset.