Mathematical programming and the optimization of computer simulations

Biles, William E.; Swain, James J.

doi:10.1007/bfb0120864

Cited by 47 publications

(16 citation statements)

References 12 publications

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“…One point that this author notes, which may seem minor but is the type of thing that often consumes an inordinate amount of time and energy, is that computer programs for the optimization procedures and the computer programs from the simulation must often be "custom fitted" together in order to perform this analysis (Biles and Swain, 1979).…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…Another statement of the generalized procedure to be followed for the case of employing a first order response surface approach to the multiple-response simulation problem is (Biles and Swain, 1979):…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…A simulation trial is conducted as each design point in the selected first order design and observations are recorded at each design point. Multiple linear regression is applied to each observation (assuming independence among the responses) producing a model for each response (Biles and Swain, 1979).…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…Another categorization states that optimization procedures fall into the three categories of (Biles and Swain, 1979):…”

Section: Optimization Techniques Using Computer Simulation Modelsmentioning

confidence: 99%

“…Computer simulation is defined as the establishment of a mathematical-logical model of a system and the experimental manipulation of that model on a digital computer (Biles and Ozmen, 1987;Biles and Swain, 1979). Four major classes of approaches to simulation optimization are described as (Pierreval, Tautou, and Bzeznik, 1995): -1.…”

Section: Optimization Techniques Using Computer Simulation Modelsmentioning

confidence: 99%

See 4 more Smart Citations

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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Section: Response Surface Methodologymentioning

confidence: 99%

Section: Response Surface Methodologymentioning

confidence: 99%

Section: Response Surface Methodologymentioning

confidence: 99%

“…Another categorization states that optimization procedures fall into the three categories of (Biles and Swain, 1979):…”

Section: Optimization Techniques Using Computer Simulation Modelsmentioning

confidence: 99%

Section: Optimization Techniques Using Computer Simulation Modelsmentioning

confidence: 99%

See 3 more Smart Citations

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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Response surface analysis of two-stage stochastic linear programming with recourse

Bailey

Jensen

Morton

1999

Naval Research Logistics

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Optimization in simulation: Current issues and the future outlook

Safizadeh

1990

Naval Research Logistics

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Simulation is commonly used to find the best values of decision variables for problems which defy analytical solutions. This objective is similar to that of optimization problems and thus, mathematical programming techniques may be applied to simulation. However, the application of mathematical programming techniques, e.g., the gradient methods, to simulation is compounded by the random nature of simulation responses and by the complexity of the statistical issues involved. The literature relevant to optimization in simulation is scattered, and no comprehensive and up‐to‐date treatment of the subject is presently available. To that end, this article brings together numerous concepts related to t he problem of optimization in simulation. Specifically, it discusses the application of mathematical programming techniques to optimization in simulation, response surface methodology and designs, perturbation analysis, and frequency domain simulation experiments. The article provides a user with an overview of the available optimization techniues and identifies future research possibilities.

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Mathematical programming and the optimization of computer simulations

Cited by 47 publications

References 12 publications

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

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

Response surface analysis of two-stage stochastic linear programming with recourse

Optimization in simulation: Current issues and the future outlook

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