In this paper methods of mixing decision rules are investigated and applied to the so-called multiple job type assignment problem with specialized servers. This problem is modeled as continuous time Markov decision process. For this assignment problem performance optimization is in general considered to be difficult. Moreover, for optimal dynamic Markov decision policies the corresponding decision rules have in general a complicated structure not facilitating a smooth implementation. On the other hand optimization over the subclass of so-called static policies is known to be tractable. In the current paper a suitable static decision rule is mixed with dynamic decision rules which are selected such that these rules are relatively easy to describe and implement. Some mixing methods are discussed and optimization is performed over corresponding classes of so-called mixing policies. These mixing policies maintain the property that they are easy to describe and implement compared to overall optimal dynamic Markov decision policies. Besides for all investigated instances the optimized mixing policies perform substantially better than optimal static policies.

中文翻译：

---

将多种作业类型分配给并行的专用服务器

本文研究了混合决策规则的方法，并将其应用于所谓的具有专用服务器的多作业类型分配问题。这个问题被建模为连续时间马尔可夫决策过程。对于这个分配问题，性能优化通常被认为是困难的。此外，对于最优动态马尔可夫决策策略，相应的决策规则通常具有复杂的结构，不利于顺利实施。另一方面，已知对所谓的静态策略的子类进行优化是易于处理的。在当前的论文中，一个合适的静态决策规则与动态决策规则混合在一起，这些规则被选择为使得这些规则相对容易描述和实现。讨论了一些混合方法，并在相应类别的所谓混合策略上执行优化。与整体最优动态马尔可夫决策策略相比，这些混合策略保持了易于描述和实现的特性。除了所有调查的实例，优化混合策略的性能明显优于优化静态策略。