Simulation Of Scheduling And Cost Effectiveness Of Nurses Using Domain Transformation Method

Abstract

Nurse scheduling is a complex combinatorial optimization problem. With increasing healthcare costs, and a shortage of trained staff it is becoming increasingly important for hospital management to make good operational decisions. A major element of hospital expenditure is staff cost. In order to help Kajang Hospital to make decisions about staffing and work scheduling, a simulation model was created to analyse the impact of alternate work schedules and investigate the optimum balance between the staffing levels of the ward and the ability to achieve good quality schedules. In this paper, we extend our novel approach to solve the nurse scheduling problem by transforming it through Information Granulation. This approach satisfies the rules of a typical hospital environment based on a real data set benchmark problem from Kajang Hospital. Generating good work schedules has a great influence on nurses` working condition which is strongly related to the level of a quality health care. Domain transformation is an approach to solving complex problems that relies on well-justified simplification of the original problem. Solution of such a simplified problem and subsequent refinement of this solution to compensate for the simplifications introduced in the first step. Compared to conventional methods, our approach involves judicious grouping (information granulation) of shifts types’ that transforms the original problem into a smaller solution domain. Later these schedules from the smaller problem domain are converted back into the original problem domain by taking into account the constraints that could not be represented in the smaller domain. An Integer Programming (IP) is formulated to solve the transformed scheduling problem by expending the branch and bound algorithm. We have used the GNU Octave, open source mathematical modelling and simulation software for Windows to solve this problem. Results from simulations on real data problem sets for a typical hospital in Malaysia shows that this algorithm facilitated computation of feasible schedules in a short time with non-critical constraints being satisfied to a large degree. The resulting solutions facilitated cost benefit analysis of different staffing levels.