By El-Kébir Boukas, Roland P. Malhamé

ISBN-10: 0387254757

ISBN-13: 9780387254753

Research, keep an eye on and Optimization of advanced Dynamic platforms gathers in one quantity a spectrum of complicated dynamic structures similar papers written through specialists of their fields, and strongly consultant of present examine tendencies. advanced platforms current very important demanding situations, in nice half because of their sheer measurement which makes it tough to understand their dynamic habit, optimize their operations, or learn their reliability. but, we are living in a global the place, as a result of expanding inter-dependencies and networking of platforms, complexity has turn into the norm. With this in brain, the amount contains elements. the 1st half is devoted to a spectrum of advanced difficulties of determination and keep an eye on encountered within the zone of construction and stock platforms. the second one half is devoted to giant scale or multi-agent procedure difficulties happening in different components of engineering equivalent to telecommunication and electrical strength networks, in addition to extra wide-spread context.

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Extra info for Analysis, Control and Optimization of Complex Dynamic Systems (Gerad 25th Anniversary)

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A:) is a probability row vector. Partition Q as where ~ 1 1 ~ ( m - m * ) x ( m - m *~) , 1 E2 ~ ( m - m * ) x m *~ and Q22 E Rm* x m * . Let 2 E1 ~ m * x ( m - m * ) , Q* = ( v l ,. . a. 8) where ( A 1 ,. . ,A') denotes a block diagonal matrix having matrix entries A 1 , . . , A 1 , and --- ( m l ,' . ,ml ) E I W ( ~ - ~ * ) ~ ~ . > It is easy to check that Q , is a generator. Let a ( . )= { a ( t ) :t 0 ) be a continuous-time Markov chain generated by Q,. The state space of a ( . ) is M = { I , .

3). Then u*(x, a ) is optimal; see Bertsekas (1987). 3). It is therefore difficult to solve these equations. In the rest of the paper, we study an approximate optimal scheme that requires solving simpler problems and yields near 46 ANALYSIS, CONTROL, AND OPTIMIZATION optimal controls. Let us give two examples as special cases to the general model. 1 (SINGLE-MACHINE SYSTEM)Consider a production system with one machine producing one part type. Let ck E {0,1) denote the machine state where 1 means the machine is up with maximum capacity 1 and 0 means that the machine is down.

A simple example is provided in Section 7. The paper is concluded in Section 8. 2. Problem formulation Consider a manufacturing system consisting of a number of machines and producing several types of products. , they are subject to breakdown and repair. For simplicity, we impose no conditions on internal buffers. Let xn E Rnl denote the surplus (inventory/shortage) and un E I? c Rn2 the rate of production. Let {a;: n 2 0) be a finite-state Markov chain with state space M = {1,2,. . , m ) and transition matrix PE= (ptj)mxm, where E is a small parameter to be specified later.

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Analysis, Control and Optimization of Complex Dynamic Systems (Gerad 25th Anniversary) by El-Kébir Boukas, Roland P. Malhamé


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