Market place requirements are putting increasing pressures on organisations to be more competitive. The requirement for performance improvement has pushed engineers and managers towards the careful study of critical parameters and towards the “re-engineering” approach. A key problem for engineers engaged in the development of powerful analytical tools is to explain their use in concrete terms to management. This problem is worsened where the value added is difficult to measure due to the nature of the process. The Health Care system is a case in point. Emergency Department crowding and rising health-care 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 Emergency Department it is important to minimise (under fixed constraints) patient waiting time; but also staff idle time while maintaining the high utilization rate of medical facilities. Traditionally, these capacity problems have been solved, mainly, by increasing the number of available resources. This paper presents some observations arising from the development of a case study in the public health care system. In particular, we developed and tested a simulation model, but also a process scheduling model of the Emergency Department (ED) of Cork University Hospital (CUH) - Ireland. Based on the analogy of a job shop scheduling problem and known patient scheduling methodologies, we used an Ant Colony Optimization (ACO) algorithm for the balancing of the process. The algorithm is based on Swarm Intelligent (SI) meta-heuristic techniques. The problem is multi-objective in its general formulation. The proposal of the present work is to optimise patient scheduling under defined precedence, zoning and capacity constraints. In addition to this goal, the approach will be to attempt to balance the workload between and within resource types (i.e., work-centres or medical staff: doctors, nurses, administrators etc…). The proposed model will be integrated into the simulation model, resulting in minimising the number of resources and balancing the workload within each resource.
The Balancing Problem in an Emergency Room based on Ant Colony Optimization algorithm
FRUGGIERO, FABIO;
2007-01-01
Abstract
Market place requirements are putting increasing pressures on organisations to be more competitive. The requirement for performance improvement has pushed engineers and managers towards the careful study of critical parameters and towards the “re-engineering” approach. A key problem for engineers engaged in the development of powerful analytical tools is to explain their use in concrete terms to management. This problem is worsened where the value added is difficult to measure due to the nature of the process. The Health Care system is a case in point. Emergency Department crowding and rising health-care 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 Emergency Department it is important to minimise (under fixed constraints) patient waiting time; but also staff idle time while maintaining the high utilization rate of medical facilities. Traditionally, these capacity problems have been solved, mainly, by increasing the number of available resources. This paper presents some observations arising from the development of a case study in the public health care system. In particular, we developed and tested a simulation model, but also a process scheduling model of the Emergency Department (ED) of Cork University Hospital (CUH) - Ireland. Based on the analogy of a job shop scheduling problem and known patient scheduling methodologies, we used an Ant Colony Optimization (ACO) algorithm for the balancing of the process. The algorithm is based on Swarm Intelligent (SI) meta-heuristic techniques. The problem is multi-objective in its general formulation. The proposal of the present work is to optimise patient scheduling under defined precedence, zoning and capacity constraints. In addition to this goal, the approach will be to attempt to balance the workload between and within resource types (i.e., work-centres or medical staff: doctors, nurses, administrators etc…). The proposed model will be integrated into the simulation model, resulting in minimising the number of resources and balancing the workload within each resource.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
