Multi-objective simulation optimization for a cancer treatment center

Baesler, Felipe F.; Sepulveda, Jose

doi:10.1109/wsc.2001.977463

Cited by 46 publications

(31 citation statements)

References 13 publications

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“…This optimization problem is then solved using a genetic algorithm. Baeslar and Sepúlveda [18] continue this research by applying their methodology to optimize a cancer treatment center.…”

Section: Simulation Optimization With Multiple Objectivesmentioning

confidence: 99%

A Review of Random Search Methods

Andradóttir

2014

International Series in Operations Research &Amp; Management Science

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This chapter provides a brief review of random search methods for simulation optimization. We start by describing the structure of random search when system performance is estimated via simulation. Next, we discuss methods for solving simulation optimization problems with discrete decision variables and one (stochastic) performance measure, with emphasis on simulated annealing. Finally, we expand our scope to address simulation optimization problems with continuous decision variables and/or multiple (stochastic) performance measures.

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“…This optimization problem is then solved using a genetic algorithm. Baeslar and Sepúlveda [18] continue this research by applying their methodology to optimize a cancer treatment center.…”

Section: Simulation Optimization With Multiple Objectivesmentioning

confidence: 99%

A Review of Random Search Methods

Andradóttir

2014

International Series in Operations Research &Amp; Management Science

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“…De Angelis et al [26] presented a methodology that uses system simulation and optimization to calculate and validate the optimal configuration of servers in a transfusion center; Zhang and Puterman [27] used simulation optimization to determine long-term care capacity levels over a multi-year planning horizon. Baesler and Sepúlveda [28] present a multi-objective simulation-optimization model for a cancer treatment center. Landa et al [29] employed simulation optimization to evaluate the best mix and resource planning in a surgery department that consisted of several surgical units.…”

Section: Introduction and Aim Of The Studymentioning

confidence: 99%

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach

Özcan

Tànfani

Testi

2016

Health Care Manag Sci

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This paper aims to improve the performance of clinical processes using clinical pathways (CPs). The specific goal of this research is to develop a decision support tool, based on a simulation-optimization approach, which identify the proper adjustment and alignment of resources to achieve better performance for both the patients and the health-care facility. When multiple perspectives are present in a decision problem, critical issues arise and often require the balancing of goals. In our approach, meeting patients' clinical needs in a timely manner, and to avoid worsening of clinical conditions, we assess the level of appropriate resources. The simulation-optimization model seeks and evaluates alternative resource configurations aimed at balancing the two main objectives-meeting patient needs and optimal utilization of beds and operating rooms.Using primary data collected at a Department of Surgery of a public hospital located in Genoa, Italy. The simulation-optimization modelling approach in this study has been applied to evaluate the thyroid surgical treatment together with the other surgery-based CPs. The low rate of bed utilization and the long elective waiting lists of the specialty under study indicates that the wards were oversized while the operating room capacity was the bottleneck of the system. The model enables hospital managers determine which objective has to be given priority, as well as the corresponding opportunity costs.

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“…First, the simulation research on health care clinics has been confined primarily to single-facility systems. Multifacility health care systems have been underrepresented in the literature (see [18]). By studying only a fragment of a subsystem, such as one clinic, the impact of the surrounding facilities may not be included in the performance measures and results may be misleading.…”

Section: Introductionmentioning

confidence: 99%

“…A terminating simulation provides a very different and more accurate operational picture than a steady-state model for an outpatient clinic. Third, the majority of simulation research on outpatient clinics has focused on single response models with less attention to multivariate response models (e.g., [18]). The performance evaluation used in many of these past outpatient analyses has been either the minimization of a patient's wait time or the minimization of the medical personnel's idle time.…”

Section: Introductionmentioning

confidence: 99%

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

Matta

Patterson²

2007

Health Care Manage Sci

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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.

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Multi-objective simulation optimization for a cancer treatment center

Cited by 46 publications

References 13 publications

A Review of Random Search Methods

A Review of Random Search Methods

Improving the performance of surgery-based clinical pathways: a simulation-optimization approach

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

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