Kriging metamodeling in constrained simulation optimization: an explorative study.

Simulation; Optimization; Studies;

Kriging metamodeling in constrained simulation optimization: an explorative study

status: publishe

Analysis of Automated Flow Line Systems with Repair Crew Interference

The interrelations between production and maintenance are mostly neglected during the design phase of automated production systems. Thus, the relevant performance measures of a planned production system like production rate, throughput time, work in process etc. are often estimated inaccurately. The paper presents an analytical approach for performance evaluation of an automated flow line system (AFLS) which takes into account the dependency between the production and the repair system. The suggested model and solution approach are particularly helpful in the initial design phase as well as during a redesign process in order to evaluate alternative configurations of the planned production and repair system

Analysis of Automated Flow Line Systems with Repair Crew Interference

The interrelations between production and maintenance are mostly neglected during the design phase of automated production systems. Thus, the relevant performance measures of a planned production system like production rate, throughput time, work in process etc. are often estimated inaccurately. The paper presents an analytical approach for performance evaluation of an automated flow line system (AFLS) which takes into account the dependency between the production and the repair system. The suggested model and solution approach are particularly helpful in the initial design phase as well as during a redesign process in order to evaluate alternative configurations of the planned production and repair system

Evaluating multiple performance measures across several dimensions at a multi-facility outpatient center

Over the past several decades healthcare delivery systems have received increased pressure to become more efficient from both a managerial and patient perspective. Many researchers have turned to simulation to analyze the complex systems that exist within hospitals, but surprisingly few have published guidelines on how to analyze models with multiple performance measures. Moreover, the published literature has failed to address ways of analyzing performance along more than one dimension, such as performance by day of the week, patient type, facility, time period, or some combination of these attributes. Despite this void in the literature, understanding performance along these dimensions is critical to understanding the root of operational problems in almost any daily clinic operation. This paper addresses the problem of multiple responses in simulation experiments of outpatient clinics by developing a stratification framework and an evaluation construct by which managers can compare several operationally different outpatient systems across multiple performance measure dimensions. This approach is applied to a discrete-event simulation model of a real-life, large-scale oncology center to evaluate its operational performance as improvement initiatives affecting scheduling practices, process flow, and resource levels are changed. Our results show a reduction in patient wait time and resource overtime across multiple patient classes, facilities, and days of the week. This research has already proven to be successful as certain recommendations have been implemented and have improved the system-wide performance at the oncology center. Copyright Springer Science+Business Media, LLC 2007Multiple response, Terminating discrete-event simulation, Performance measures, Outpatient scheduling, Healthcare applications, Multi-facility service system, Oncology center system analysis,