Computer simulation and swarm intelligence organisation into an emergency department: a balancing approach across Ant Colony Optimisation

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.

Robust capacity planning for accident and emergency services

“…Another comprehensive review of simulation modelling studies in emergency departments for normal and disaster conditions can be found in Gul et al [27]. Among 106 reviewed papers, only few studies consider an optimization approach [1,18,22,51,59]. Fruggiero et al [22] uses ant-colony optimization along with a simulation model to optimize the resources in an emergency department.…”

“…Among 106 reviewed papers, only few studies consider an optimization approach [1,18,22,51,59]. Fruggiero et al [22] uses ant-colony optimization along with a simulation model to optimize the resources in an emergency department. Weng et al [59], Ibrahim et al [33] and Rico et al [51] use OptQuest, a SO engine [49], to optimize nurse and physician numbers and nurse allocation in an influenza outbreak, respectively.…”

Toward a decision support system for the clinical pathways assessment

“…Simulation techniques have mainly been used in the literature for the analysis of healthcare systems. Amongst these techniques, the most frequent modeling methodologies are discrete event simulation [20,33,25,36,19,27,30,31,45,49,51] and system dynamics [26,2]. Simulation techniques do not suffer from the state explosion problem and are a good alternative to state-based techniques, nevertheless the time needed to get a required accuracy is often prohibitive.…”

A proposed simulation optimization model framework for emergency department problems in public hospital

“…Azcarate et al [32] used a metaheuristic and simulation approach to analyze management problems in a healthcare context. Fruggiero et al [33] combined computer simulation and ant colony optimization to optimize patient scheduling while balancing the workload between and within resource types. Baesler and Sepulveda [34] present a multiobjective simulation optimization model for a cancer treatment.…”