How to conduct a successful simulation study

Law,

doi:10.1109/wsc.2003.1261409

Cited by 34 publications

(22 citation statements)

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“…We used the framework proposed by Law (2003) to design the simulation model and conduct an analysis of the mill process fl ow, consisting of the following seven steps: formulate the problem; collect information/data and construct conceptual model; verify validity of the conceptual model; program the model; validate the programming model; design, conduct and analyze experiments; and, document and present simulation results.…”

Section: Methodsmentioning

confidence: 99%

Using Flow Simulation as a Decision Tool for Improvements in Sawmill Productivity

Thoews

Maness

Ristea

2008

Maderas, Cienc. tecnol.

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We developed a sawmill fl ow simulation model to identify production bottlenecks and determine where productivity improvements could be made. Sawmills often invest in a new machine center and then fi nd out that the processing bottleneck just moves somewhere else. Our approach was specifi cally designed to investigate the effects of such changes on the entire system. We determined that the trimmer was the system bottleneck when both the small log and large log lines were running concomitantly. Under base case conditions, the model predicted an average board output of 13,147 boards. An increase in the processing capability of the trimmer resulted in a shift of the bottleneck from the small log line to the large log line (at the edger). This bottleneck shift was further investigated and, by allowing the simulation model to manipulate machine settings for the trimmer and edger, it was able to maximize the modeled average board output to 17,996 boards per shift (when edger set up times were not considered) and 16,708 boards per shift (with edger setup times included). These fi ndings were presented to the sawmill management and subsequently implemented as specifi c improvements at the trimmer machine center, which in turn resulted in an actual increase of 10% in their sawmill's lumber volume output.

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Section: Methodsmentioning

confidence: 99%

Using Flow Simulation as a Decision Tool for Improvements in Sawmill Productivity

Thoews

Maness

Ristea

2008

Maderas, Cienc. tecnol.

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“…• The event list is empty • The arrival of task termination event for the final task (i.e., the Nth task) is executed Entities component: Entities are the representation of objects in the system (Karatza, 2000;Law, 2003). Fig.…”

Section: Referring Tomentioning

confidence: 99%

“…The urgency of a task is captured as a deadline on X-axis and the importance of a task is measured by utility in Y-axis. (Law, 2003) With reference to Fig. 1, in the event of the task being computed at time A, which denotes the range between the start of execution and the stipulated deadline, the system gains a positive utility.…”

Section: Introductionmentioning

confidence: 99%

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A Discrete Event Simulation Framework for Utility Accrual Scheduling Algorithm in Uniprocessor Environment

Ahmad

Subramaniam²,

Othman³

et al. 2011

Journal of Computer Science

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Problem statement:The heterogeneity in the choice of simulation platforms for real time scheduling stands behind the difficulty of developing a common simulation environment. A Discrete Event Simulation (DES) for a real time scheduling domain encompassing event definition, time advancing mechanism and scheduler has yet to be developed. Approach: The study focused on the proposed and the development of an event based discrete event simulator for the existing General Utility Scheduling (GUS) to facilitate the reuse of the algorithm under a common simulation environment. GUS is one of the existing TUF/UA scheduling algorithms that consider the Time/Utility Function (TUF) of the executed tasks in its scheduling decision. The scheduling optimality criteria are based on maximizing accrued utility accumulated from execution of all tasks in the system. These criteria are named as Utility Accrual (UA). The TUF/ UA scheduling algorithms are design for adaptive real time system environment. The developed GUS simulator has derived the set of parameter, events, performance metrics and other unique TUF/UA scheduling element according to a detailed analysis of the base model. Results: The Accrued Utility Ratio (AUR) is investigated and compared to the benchmark of the modeled domain. Successful deployment of the GUS simulator was proven by the generated results. Conclusion: Extensive performance analysis of GUS simulator can be deployed using the developed simulator with low computational overhead. Further enhancements were to extend the developed GUS simulator with detail performance metrics together with a fault tolerance mechanism to support a reliable real time application domain.

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“…"Validation is the process of determining whether a simulation model is an accurate representation ofthe system, for the particular objectives of the study" (Law 2003). "Validity, is ajudgment regarding how well suited a particular representation is for a specific application" (Hughes and Rolek 2003).…”

Section: A Reviewmentioning

confidence: 99%

Increased production capabilities by job rotation through simulation.

Cramer¹

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How to conduct a successful simulation study

Cited by 34 publications

References 0 publications

Using Flow Simulation as a Decision Tool for Improvements in Sawmill Productivity

Using Flow Simulation as a Decision Tool for Improvements in Sawmill Productivity

A Discrete Event Simulation Framework for Utility Accrual Scheduling Algorithm in Uniprocessor Environment

Increased production capabilities by job rotation through simulation.

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