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Discrete manufacturing process design optimization using computer simulation and generalized hill climbing algorithms

机译：使用计算机仿真和广义爬山算法的离散制造工艺设计优化

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Discrete manufacturing process designs can be modelled using computer simulation. Determining optimal designs using such models is very difficult, due to the large number of manufacturing process sequences and associated parameter settings that exist. This has forced researchers to develop heuristic strategies to address such design problems. This paper introduces a new general heuristic strategy for discrete manufacturing process design optimization, called generalized hill climbing (GHC) algorithms. GHC algorithms provide a unifying approach for addressing such problems in particular, and intractable discrete optimization problems in general. Ileuristic strategies such as simulated annealing, threshold accepting, Monte Carlo search, local search, and tabu search (among others) can all be formulated as GHC algorithms. Computational results are reported with various GHC algorithms applied to computer simulation models of discrete manufacturing process designs under study at the Materials Process Design Branch of Wright Laboratory, Wright Patterson Air Force Base (Dayton, Ohio, USA).
机译：离散制造过程设计可以使用计算机仿真来建模。由于存在大量的制造过程序列和相关的参数设置，因此使用此类模型确定最佳设计非常困难。这迫使研究人员开发启发式策略来解决此类设计问题。本文介绍了一种用于离散制造过程设计优化的新的通用启发式策略，称为广义爬山（GHC）算法。 GHC算法提供了一种统一的方法来解决这些问题，尤其是解决棘手的离散优化问题。诸如模拟退火，阈值接受，蒙特卡洛搜索，局部搜索和禁忌搜索（以及其他）之类的虚伪策略都可以表述为GHC算法。在Wright Patterson空军基地Wright实验室（美国俄亥俄州代顿）的Wright实验室材料工艺设计分公司研究中，使用各种GHC算法将计算结果报告给离散制造工艺设计的计算机仿真模型。

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