A guided particle swarm optimization algorithm for nurse scheduling problem

Abstract

The Nurse Scheduling Problem (NSP) is a combination of optimization problem and important management functions performed by nurses which directly affected the hospital services and the patient care. The NSP is varied which tends to be solved with nature inspired search approaches such as particle swarm optimization (PSO). Hence, the main purpose of this paper is to utilize the PSO to find the balance of nurse assignment by considering the coverage demand and nurse preferences. The actual task of generating the nurse schedule is investigated in one of the public hospitals in Malaysia. The proposed algorithms are tested on the real data and the obtained results demonstrate that the proposed particle swarm optimization has better performance to cope with complex scheduling problem. The experimental results show that the guided randomly PSO algorithm is more effective in term of the time constraint and the feasible assignment solution.

Improved Bacterial Foraging Optimization Algorithm with Information Communication Mechanism for Nurse Scheduling

“…Equation (5) means that each nurse may work no more than the large limit of work shifts and no less than lower limit of work shifts. Equation (6) shows that each schedule must satisfy the hospital's minimum coverage demands.…”

“…This would consume considerable time without even meeting most constraints. Therefore, a large variety of evolutionary computation approaches have been studied to find a better scheme for NSP, including tabu search [2], scatter search [3], Bayesian network [4], genetic algorithm [5], particle swarm optimization [6], variable neighborhood search [7] [8] and so on.…”