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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Optimization of production systems through integration of computer simulation, design of experiment, and Tabu search: the case of a large steelmaking workshop
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Optimization of production systems through integration of computer simulation, design of experiment, and Tabu search: the case of a large steelmaking workshop

机译：通过集成计算机仿真，实验设计和禁忌搜索来优化生产系统：大型炼钢车间的案例

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The objective of this study is to optimize the performance of discrete production systems by integration of computer simulation, design of experiment (DOE), and Tabu search (TS). Optimizing performance of a steelmaking workshop was considered as the case of this study, but it could be used to optimize the throughput of other production system. The simulation model is built by considering all major and detailed operations and interacting systems of the workshop. The results and the structure of the integrated simulation model are verified and validated by t test. To integrate simulation outputs with DOE, decision making parameters are defined as number of machines, operators, etc. (k factors). To estimate and assess the effects of each of the factors and their two-way interactions on response variable, a complete 3 k factorial design with lower and upper limits and a center point is considered. Furthermore, response surface methodology (RSM) is used to optimize the response variable. Because a first-order model may not be adequate for the RSM, a polynomial order regression equation is developed by least square method. By steepest ascent, the local optimum is identified. However, the global optimal solution is computed by Tabu search which uses a metaheuristic approach. Previous studies use integration of DOE and simulation to find optimum alternative. This is usually conducted by RSM and steepest ascent which locates local optimum solution. However, integration of DOE and TS locates global optimum solution.
机译：这项研究的目的是通过集成计算机仿真，实验设计（DOE）和禁忌搜索（TS）来优化离散生产系统的性能。本研究以优化炼钢车间的性能为例，但可以用来优化其他生产系统的生产能力。通过考虑车间的所有主要和详细操作以及交互系统来构建仿真模型。通过t检验对集成仿真模型的结果和结构进行了验证。为了将仿真输出与DOE集成在一起，决策参数定义为机器，操作员等的数量（k个因子）。为了估计和评估每个因子及其双向交互作用对响应变量的影响，考虑了具有上下限和中心点的完整3 k 析因设计。此外，响应面方法（RSM）用于优化响应变量。由于一阶模型可能不适用于RSM，因此通过最小二乘法开发了多项式阶次回归方程。通过最陡峭的上升，可以确定局部最优。但是，全局最优解是通过使用元启发式方法的禁忌搜索来计算的。先前的研究使用DOE和仿真的集成来找到最佳替代方案。这通常是由RSM和最陡峭的上升进行的，该上升确定了局部最优解。但是，DOE和TS的集成找到了全局最佳解决方案。

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