142 Int. J. Services Operations and Informatics, Vol. 3, No. 2, 2008 Copyright © 2008 Inderscience Enterprises Ltd. Computer simulation and swarm intelligence organisation into an emergency department: a balancing approach across ant colony optimisation Fabio Fruggiero* and Alfredo Lambiase Department of Mechanical Engineering, University of Salerno, Via Ponte Don Melillo 1, 84084 Fisciano, SA, Italy E-mail: ffruggiero@unisa.it E-mail: lambiase@unisa.it *Corresponding author Daithí Fallon Department of Manufacturing, Biomedical & Facilities Engineering, Cork Institute of Technology (CIT), Rossa Avenue, Bishopstown, Cork, Ireland E-mail: Daithi.Fallon@cit.ie Abstract: Healthcare system must be sensitive to the needs of patient, financially viable and cost-effective. Emergency Department (ED) crowding and rising healthcare costs are perceived as significant issues that are getting worse. In order to respond to the growing number of incoming patients, hospital departments, including emergency rooms, have to re-evaluate their current facilities, procedures and practises from an operations management perspective. In a typical ED, it is important to minimise not only the patient’s waiting time but also the staff idle time while maintaining the high utilisation rate of medical facilities. Computer simulation is recognised as a powerful tool, for medical management, to enquire productivity trying to increase service level to patients. Based on the analogy of a Job Shop Scheduling Problem (JSSP) and known patient scheduling methodologies, a metaheuristic Swarm Intelligence (SI) approach, focused on Ant System (AS) behaviour, was used in the balancing of an ED. The Ant Colony Optimisation (ACO) algorithm was implemented with the proposal to optimise patient scheduling under defined precedence, zoning and capacity constraints while balancing the workload between and within resource types. The ED of Cork University Hospital (CUH), Ireland, is the case in issue. Keywords: healthcare; Emergency Department; ED; computer simulation; line balancing; Swarm Intelligence; SI; Ant System; AS; Ant Colony Optimisation; ACO. Reference to this paper should be made as follows: Fruggiero, F., Lambiase, A. and Fallon, D. (2008) ‘Computer simulation and swarm intelligence organisation into an emergency department: a balancing approach across ant colony optimisation’, Int. J. Services Operations and Informatics, Vol. 3, No. 2, pp.142–161.