Monotonicity in Markov Reward and Decision Chains: Theory by Ger Koole, Visit Amazon's G. M. Koole Page, search results,

By Ger Koole, Visit Amazon's G. M. Koole Page, search results, Learn about Author Central, G. M. Koole,

Monotonicity in Markov present and determination Chains: conception and functions makes a speciality of monotonicity effects for dynamic structures that take values within the typical numbers or in more-dimensional lattices. the consequences are regularly formulated by way of managed queueing platforms, yet there also are purposes to upkeep structures, profit administration, etc. the focal point is on effects which are bought by way of inductively proving homes of the dynamic programming worth functionality. A framework is supplied for utilizing this system that unifies effects bought for various versions. the writer additionally presents a complete evaluate of the consequences that may be acquired via it, within which he discusses not just (partial) characterizations of optimum rules but additionally purposes of monotonicity to optimization difficulties and the comparability of platforms. Monotonicity in Markov present and choice Chains: conception and functions is a useful source for an individual making plans or engaging in study during this specific zone. The necessities of the subject are provided in an obtainable demeanour and an in depth bibliography courses in the direction of extra examining.

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Additional info for Monotonicity in Markov Reward and Decision Chains: Theory and Applications (Foundations and Trends in Stochastic Systems)

Example text

A characteristic result is the optimality of the µc rule. 1. The following hold, for 1 ≤ i ≤ m: — TA(i) , TCA(i) (with µ(1) ≤ · · · ≤ µ(m)), TMS , Tdisc , Tenv , Tmin (with µ(1) ≤ · · · ≤ µ(m)), Tmax (with µ(1) ≥ · · · ≥ µ(m)): wUI → wUI; 45 46 Models — TA(i) , TCA(i) (with µ(1) ≤ · · · ≤ µ(m)), TMS , TMMS (with µ(1) ≤ · · · ≤ µ(m)), Tdisc , Tenv , Tmin (with µ(1) ≤ · · · ≤ µ(m)), Tmax (with µ(1) ≥ · · · ≥ µ(m)): I ∩ wUI → I ∩ wUI; — TA(i) , TMTS , Tdisc , Tenv : gUI → gUI, where the µs in the inequalities are equal to the µs in the movable server operators.

3 Continuous Time 21 with TCA the controlled arrival operator and with TDj the j-fold convolution of the departure operator TD , and with p(j), the coefficient of TDj Vn , the probability that j (potential) departures occur during an inter-arrival time. ) Embedding can also be useful if the transition rates are unbounded, as in the M/M/∞ queue. Note that in the M/M/∞ queue there is no lower bound for the expected time that we stay in a state. Therefore uniformization cannot be used. A way around is only looking at arrival instances, again with the consequence that there is a random number of departures during each period.

We prove TCA : Cx → Cx. We need to check the different possibilities for the minimizers in x and x + 2ei , denoted by a1 and a2 , respectively. The only non-trivial case is a1 = a2 . It is readily seen that 2 min{c + f (x + ei ), c + f (x + 2ei )} ≤ c + f (x + ei ) + c + f (x + 2ei ), which corresponds to the case that a1 corresponds to admission and a2 to rejection, and 2 min{c + f (x + ei ), c + f (x + 2ei )} ≤ c + f (x) + c + f (x + 3ei ) (by using convexity twice), which corresponds to the remaining case (which in fact cannot occur, as we will see later).

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