Read e-book online Average—Cost Control of Stochastic Manufacturing Systems PDF

By Suresh P. Sethi

ISBN-10: 0387219471

ISBN-13: 9780387219479

ISBN-10: 0387276157

ISBN-13: 9780387276151

This publication is worried with hierarchical keep watch over of producing structures less than uncertainty. It makes a speciality of approach functionality measured in long-run ordinary price standards, exploring the connection among keep an eye on issues of a reduced fee and that with a long-run regular price in reference to hierarchical keep an eye on. a brand new thought is articulated that indicates that hierarchical determination making within the context of a goal-seeking production procedure may end up in a close to optimization of its target. The strategy within the e-book considers production platforms during which occasions ensue at various time scales.

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Additional resources for Average—Cost Control of Stochastic Manufacturing Systems

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The plan of the chapter is as follows. 2 we precisely state the production–inventory model under consideration. 3 we establish a systematic approach of constructing the ergodic (stable) control policies. In addition, we develop some estimates for the value function of the corresponding discounted cost problems. 3 and the use of the vanishing discount approach, a solution of the HJB equation is shown to exist. 2 Problem Formulation 25 minimum average cost and a potential function, both of which are related to the value function of the corresponding discounted problem.

13) where and x(·) is the surplus process corresponding to the control u(·) and the initial condition (x(0), k(0)) = (x, k). Proof. We provide a proof only in the case when (x, k) = (0, 0). The proofs in all other cases are similar. Recall that {νk : k = 0, 1, . . , lim P (k(t) = k|k(0) = i) = νk , t→∞ k, i ∈ M. Notice that, by Assumption (A3), k(·) is a strongly irreducible Markov chain, so we have νk > 0, k = 0, 1, . . , m. Since M is a finite set, we have a t0 > 0 such that, for all t ≥ t0 , P (k(t) = k|k(0) = i) ≥ νk /2, k, i ∈ M.

For any u(·) ∈ A(k), the dynamics of the system is d x(t) = u(t) − z, dt x(0) = x, t ≥ 0. 2. A function u(·, ·) defined on × M is called an admissible feedback control, or simply feedback control, if: (i) for any given initial surplus x and production capacity k, the equation d x(t) = u(x(t), k(t)) − z dt has a unique solution; and (ii) the process u(·) = {u(t) = u(x(t), k(t)), t ≥ 0} ∈ A(k). With a slight abuse of notation, we shall express the admissibility condition (ii) simply as the function u(·, ·) ∈ A(k).

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Average—Cost Control of Stochastic Manufacturing Systems by Suresh P. Sethi


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