A Goal Programming Approach to the Optimization of Multi response Simulation Models

Clayton, Edward R.; Weber, Warren E.; Taylor, Bron

doi:10.1080/05695558208975241

Cited by 33 publications

(18 citation statements)

References 2 publications

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“…8. Clayton, Weber, and Taylor (1982) present a direct search goal programming approach designed to handle deterministic simulation outputs with strict integer decision vectors. The search technique is based on the discrete parameter version of the pattern search procedure developed by Hooke and Jeeves (1961) and is modified within the framework of preemptive goal programming to handle multiple performance measures.…”

Section: Methods Using Progressive Articulation Of Preferencesmentioning

confidence: 99%

Optimization of systems with multiple performance measures via simulation: Survey and recommendations

Rosen

Harmonosky

Traband

2008

Computers & Industrial Engineering

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Section: Methods Using Progressive Articulation Of Preferencesmentioning

confidence: 99%

Optimization of systems with multiple performance measures via simulation: Survey and recommendations

Rosen

Harmonosky

Traband

2008

Computers & Industrial Engineering

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“…Few published studies, however, have tackled the problem of how to evaluate models based on several response variables (e.g., [18,19]). In fact, outside of the outpatient literature, only a few attempts have been made to solve the multi-objective simulation optimization problem, referred to as the multiple response problem (e.g., [18][19][20][21][22][23][24]). To the best of the authors' knowledge, no published literature has addressed how to tackle the problem presented here where hundreds of performance measures are tracked with each simulation replication and then analyzed over many alternative models.…”

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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“…This third technique is a method for optimizing multiple response simulation models that applies modified pattern search and gradient search techniques to the simulation model responses that may be linear or nonlinear. For this procedure the model equations generating the responses may be known or unknown, however, the simulation must not contain Monte-Carlo random effects (Clayton et al, 1982).…”

Section: Goal Programming With Preemptive Prioritiesmentioning

confidence: 99%

“…These are factor screening experiments, experiments of comparison, and response surface methodology (Biles and Ozmen, 1987). Another view of optimizing the output proposes the steps (Clayton, Weber, and Taylor, 1982):…”

Section: Optimization Techniques Using Computer Simulation Modelsmentioning

confidence: 99%

The Compass Paradigm for the Systematic Evaluation of U.S. Army Command and Control Systems Using Neural Network and Discrete Event Computer Simulation

Middlebrooks¹,

Jones²,

Henry³

2005

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(ABSTRACT)In today's technology based society the rapid proliferation of new machines and systems that would have been undreamed of only a few short years ago has become a way of life. Developments and advances especially in the areas of digital electronics and micro-circuitry have spawned subsequent technology based improvements in transportation, communications, entertainment, automation, the armed forces, and many other areas that would not have been possible otherwise. This rapid "explosion" of new capabilities and ways of performing tasks has been motivated as often as not by the philosophy that if it is possible to make something better or work faster or be more cost effective or operate over greater distances then it must inherently be good for the human operator. Taken further, these improvements typically are envisioned to consequently produce a more efficient operating system where the human operator is an integral component. The formal concept of human-system interface design has only emerged this century as a recognized academic discipline, however, the practice of developing ideas and concepts for systems containing human operators has been in existence since humans started experiencing cognitive thought.An example of a human system interface technology for communication and dissemination of written information that has evolved over centuries of trial and error development, is the book. It is no accident that the form and shape of the book of today is as it is. This is because it is a shape and form readily usable by human physiology whose optimal configuration was determined by centuries of effort and revision. This slow evolution was mirrored by a rate of technical evolution in printing and elsewhere that allowed new advances to be experimented with as part of the overall use requirement and need for the existence of the printed word and some way to contain it. Today, however, technology is advancing at such a rapid rate that evolutionary use requirements have no chance to develop along side the fast pace of technical progress. One result of this recognition is the establishment of disciplines like human factors engineering that have stated purposes and goals of systematic determination of good and bad human system interface designs. However, other results of this phenomenon are systems that get developed and placed into public use simply because new technology allowed them to be made. This development can proceed without a full appreciation of how the system might be used and, perhaps even more significantly, what impact the use of this new system might have on the operator within it.The U.S. Army has a term for this type of activity. It is called "stove-piped development." The implication of this term is that a system gets developed in isolation where the developers are only looking "up" and not "around." They are thus concerned only with how this system may work or be used for its own singular purposes as opposed to how it might be used in the larger community of existing systems and interfaces...

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A Goal Programming Approach to the Optimization of Multi response Simulation Models

Cited by 33 publications

References 2 publications

Optimization of systems with multiple performance measures via simulation: Survey and recommendations

Optimization of systems with multiple performance measures via simulation: Survey and recommendations

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

The Compass Paradigm for the Systematic Evaluation of U.S. Army Command and Control Systems Using Neural Network and Discrete Event Computer Simulation

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