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The value-iteration method is often the most preferred method to compute a (nearly) average cost optimal policy In each iteration of the method the lower and upper bounds indicate how much the average cost of the current policy deviates from the minimal average cost The computational burden of the value-iteration algorithm depends not only on the number of states, but also on the density of the non-zero transition probabilities pij (a) By the very nature of the value-iteration algorithm, it is computationally burdensome to have many non-zero pij (a) In applications with exponentially distributed times between the decision epochs, the computational effort of the value-iteration algorithm can often be considerably reduced by including so-called ctitious decision epochs The state of the system is left unchanged at the ctitious decision epochs The inclusion of ctitious decision epochs does not change the Markovian nature of the decision process, since the times between state transitions are exponentially distributed and thus have the memoryless property The trick of ctitious decision epochs reduces not only the computational effort, but also simpli es the formulation of the value-iteration algorithm The inclusion f ctitious decision epochs has as a consequence that the state space must be enlarged with an indicator variable to distinguish between the ctitious decision epochs and the real decision epochs However, the greater simplicity in formulation and the reduction in computing times outweigh the enlarged state space Example 731 Optimal allocation of servers to competing customers In communication networks an important problem is the allocation of servers to competing customer classes Suppose messages of the types 1 and 2 arrive at a communication system according to independent Poisson processes with respective rates 1 and 2 The communication system has c identical transmission channels for handling the messages, where each channel can handle only one message at a time The system has no buffer for storing temporarily messages that nd all channels occupied upon arrival Such messages have to be rejected anyway However, a newly arriving message may also be rejected when there is a free channel The goal is to nd a control rule that minimizes the average rejection rate or, equivalently, maximizes the average throughput of accepted messages In Example 542 the best control rule was determined within the subclass of L-policies Markov decision theory enables us to compute an overall optimal policy To do so, we.

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