Design and Analysis of Simulation Experiments

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Skip to search form Skip to main content. This review surveys classic and modern designs for experiments with simulation models. Classic designs were developed for real, non-simulated systems in agriculture, engineering, etc. These designs assume a few factors no more than ten factors with only a few values per factor no more than five values.

These designs are mostly incomplete factorials e. View via Publisher. Open Access.

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Share This Paper. Figures, Tables, and Topics from this paper. Figures and Tables. Citations Publications citing this paper. Implementing the aforementioned simulation methodologies described in 3 using available software, for example, AnyLogic, MS Excel or R. Determining and Analyzing burn-in periods and simulation run times to achieve statistically reliable estimates of simulated system performance.

Tails in Biomimetic Design: Analysis, Simulation, and Experiment | MIT Biomimetics Robotics Lab

Recommending and Ranking competing systems by Designing simulation experiments as well as Employing statistical inference and variance reduction techniques for comparison studies. Undergraduate Courses.


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    Associated Data

    One of the controversial issues in manufacturing companies is bottleneck. Managers and engineers try to deal with this difficulty to improve the productivity such as increasing resource utilization and throughput.

    One color factory is selected as a case study in this paper. This company tries to identify and decrease the bottlenecks in the production line.

    AMS162: Design and Analysis of Computer Simulation Experiments

    The goal of this paper is building the simulation model of production line to improve the productivity by analyzing the bottleneck. To achieve this goal, statistical method named design of experiment DOE was performed in order to find the optimum combination of factors that have the significant effect on the process productivity.

    The analysis shows that all of the main factors have a significant effect on the production line productivity. The optimum value of productivity is achieved when the number of delpak mixer C and number of lifter D to be located at high level that is equal to 2 and 2 respectively. The most significant conclusion of this study is that 3. It means that 3 full time labors and one part time labor should be employed for the production line.

    Sun, G.

    A comprehensive literature classification of simulation optimisation methods

    Centeno, M. An Introduction to Simulation Modeling. Proceedings of the Winter Simulation Conference.