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Approximate solutions for large-scale piecewise deterministic control systems arising in manufacturing flow control models

机译:制造流程控制模型中产生的大规模分段确定性控制系统的近似解决方案

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摘要

We propose a numerical technique for approximately solving large-scale piecewise deterministic control systems that are typically related to manufacturing flow control problems in unreliable production systems. The method consists of reformulating the stochastic control problem under study into a Markov decision process. Then we exploit the associated dynamic programming conditions and we propose an "approximate" policy iteration algorithm. This will be based on an approximation of the Bellman functions by a combination of a set of base functions, using a specific decomposition technique. The numerical method is applicable whenever a turnpike property holds for some associated infinite horizon deterministic control problem. To illustrate the approach, we solve an example and compare this new approximation method with a more classical approximation-by-decomposition technique.
机译:我们提出了一种数值技术,用于近似解决大规模分段确定性控制系统,该系统通常与不可靠的生产系统中的制造流程控制问题有关。该方法包括将正在研究的随机控制问题重新构造为马尔可夫决策过程。然后,我们利用相关的动态编程条件,并提出了一种“近似”策略迭代算法。这将基于使用特定分解技术的一组基本函数的组合对Bellman函数的近似。每当收费公路属性适用于某些关联的无限层确定性控制问题时,都可以应用数值方法。为了说明该方法,我们解决了一个示例,并将这种新的近似方法与更经典的逐个分解近似技术进行了比较。

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