Download Computing Tools for Modeling, Optimization and Simulation: by Fred Glover (auth.), Manuel Laguna, José Luis González PDF

By Fred Glover (auth.), Manuel Laguna, José Luis González Velarde (eds.)

Computing instruments for Modeling, Optimization and Simulation displays the necessity for conserving the wedding among operations examine and computing so one can create extra effective and strong software program instruments within the years forward. The 17 papers integrated during this quantity have been rigorously chosen to hide a variety of themes relating to the interface among operations study and machine technological know-how. the amount contains the now perennial functions of rnetaheuristics (such as genetic algorithms, scatter seek, and tabu seek) in addition to examine on international optimization, wisdom administration, software program rnaintainability and object-oriented modeling. those themes replicate the complexity and diversity of the issues that present and destiny software program instruments has to be able to tackling. The OR/CS interface is often on the center of profitable functions and the advance of recent methodologies, making the examine during this ebook a suitable reference sooner or later.
The editors' target for this ebook has been to extend the curiosity within the interface of machine technological know-how and operations learn. either researchers and practitioners will reap the benefits of this publication. the academic papers may well spark the curiosity of practitioners for constructing and utilising new thoughts to complicated difficulties. furthermore, the ebook contains papers that discover new angles of well-established equipment for difficulties within the region of nonlinear optimization and combined integer programming, which professional researchers in those fields could locate fascinating.

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Extra resources for Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research

Sample text

This (optimal) strategy has the following natural interpretation. e. one for which the ratio 5.. Pi is the smallest. Let us consider next globally parallel SPSs of depth 2. By the same token, one might want to inspect the subsystems in the increasing order of ECi P; , where now ECi denotes the expected cost of inspection, and P; denotes the probability of working for the ;Ih subsystem. Ben-Dov (1981) proposed such an algorithm, which essentially replaces each subsystem with a single component whose inspection cost is the optimal expected cost of that subsystem and whose probability of functioning is the probability that the subsystem functions.

Even if a software/hardware combination could handle all of the points at once, it might be very time consuming to construct rules using hundreds of thousands of points. To overcome this impracticality, we propose the following three-step approach. First, extract many subsets of points from the original data set for M. Laguna et al. ), Computing Tools for Modeling, Optimization and Simulation © Springer Science+Business Media New York 2000 26 Fu, el al. analysis. 5 software due to Quinlan (1993».

Let us consider a 2-level deep SPS r(N,f), consisting of the parallel connection of m simple series subsystems. Let us denote the i 'h subsystem by ri(Ni ,/;), where N i = {u i 'U i I 1 '''''U i . } , 1(,) and /; I(i) = j=1 AX . , m. , m. :::;; ... :::;;~ qi, qi1 qil(i) (4) 53 Sequential Testing ofSeries-Parallel Systems ofSmall Depth Let C i = EC(fi ) be the optimal expected cost of inspection, and the probability of functioning correctly for subsystem f i Qi =1- , P; be and define P; . g. / = p.

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