A Java-based simulation manager for optimization and response surface methodology in multiple-response parallel simulation

Biles, William E.; Kleijnen, J.P.C.

doi:10.1145/324138.324311

Cited by 9 publications

(10 citation statements)

References 4 publications

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“…In a parallel processing system RSM approach, the desire is to determine the 2 n-p + 2N + P design points in a central composite design and to systematically allocate the determination of these points to P client processors (Biles and Kleijnen, 1999).…”

Section: Response Surface Methodologymentioning

confidence: 99%

“…Since a simulation study typically involves executing R replications of the model at each of S sets of input conditions, the server's task in managing a parallel replications approach is to allocate the RS x simulation trials to P client processors in a manner that balances the workload on each processor. The objective is to complete the simulation study in a time interval approaching 1/P of that which would be required of a single processor operating in a purely sequential mode (Biles and Kleijnen, 1999).…”

Section: Doe Disadvantagesmentioning

confidence: 99%

“…The first phase consists of a first order experimental design that estimates an "optimal" improving direction and a second phase that determines the "optimal" step in the direction of the first phase. An advantage of this technique is that each phase can be tailored to the assignment of a set of S simulation trials to P parallel processors (Biles and Kleijnen, 1999).…”

Section: Gradient Based Optimization Approachmentioning

confidence: 99%

“…Each of the P processors acts, analogically, as an island continent for the purposes of evolution, where the evolutionary process takes place as if it were completely unaware of the existence of its "neighbor" continents. After a complete GA search has taken place on each of the P client processors, the Simulation Manager receives the collective statistical results and determines the best solution (Biles and Kleijnen, 1999).…”

Section: Gradient Based Optimization Approachmentioning

confidence: 99%

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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: Doe Disadvantagesmentioning

confidence: 99%

Section: Gradient Based Optimization Approachmentioning

confidence: 99%

Section: Gradient Based Optimization Approachmentioning

confidence: 99%

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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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“…rl--This paper explores the simulation-study capabilities of a Java-based simulation manager such as that discussed by Biles and Kleijnen (1999). Theoretically, a simulation study that requires R replications of each of S system scenarios to obtain the desired confidence intervals on each of M performance measures can be achieved in the time T, to run just one complete replication on the slowest available engine processor plus overhead time To.…”

mentioning

confidence: 99%

A Java-based simulation manager for Web-based simulation

Marr¹,

Storey²,

Biles³

et al.

2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165)

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This paper discusses a Web-based simulation manager program that executes an Application Service Provider (ASP) event for a customer who does not possess the inhouse capability to program complex simulations. The utility in using this simulation manager is that the customer needs results in near real-time; that is, approximately the time to run one complete replication of the simulation model plus some overhead time to send the commands necessary to execute the simulation and to process the results. The simulation manager executes simulation studies in a parallel replications format, using either designed experiments or optimization methodologies, by sending the appropriate messages to a set of engine processors to cause the execution of the prescribed simulation trials. It then receives and analyzes the simulation results produced by the simulation engines, and sends a project report to the human customer.

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Distributed discrete event simulation using the three-phase approach and Java

Cassel

Pidd

2001

Simulation Practice and Theory

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A Java-based simulation manager for optimization and response surface methodology in multiple-response parallel simulation

Cited by 9 publications

References 4 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

A Java-based simulation manager for Web-based simulation

Distributed discrete event simulation using the three-phase approach and Java

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