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forwarding-class less-than-best-effort { loss-priority high code-points cs1; } } inet-precedence inet-prec { forwarding-class expedited-forwarding { loss-priority high code-points [ 101 110 111 ]; } forwarding-class assured-forwarding { loss-priority high code-points 011; } forwarding-class best-effort { loss-priority high code-points [ 000 010 100 ]; } forwarding-class less-than-best-effort { loss-priority high code-points 001; } } } drop-profiles { aggressive { fill-level 10 drop-probability 30; fill-level 70 drop-probability 100; } gentle { fill-level 20 drop-probability 20; fill-level 80 drop-probability 80; } } forwarding-classes { queue 0 best-effort; queue 1 less-than-best-effort; queue 2 assured-forwarding; queue 3 expedited-forwarding; } interfaces { so-0/*/* { scheduler-map diffserv; unit * { classifiers { dscp diffserv; exp mpls-exp; } } } ge-2/*/* { scheduler-map diffserv; unit * { classifiers { inet-precedence nondiffserv; exp mpls-exp; } } }



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As with other search algorithms, the GA needs to remember good solutions and discard bad ones if it is to make progress towards the optimum. A very simple selector would be to select the top N chromosomes from each population for progression to the next population. This would work up to a point, but any solutions which have very high tness will always make it through to the next population. This concept is known as elitism and will be covered later in this chapter. However, to make sure that the GA doesn t converge on a set of solutions too quickly, a random element is usually introduced into the selection procedure. The following section describes the roulette wheel selector, which is one of the most popular procedures along with the tournament selector.





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When there is not sufficient on-site space available in which to store inventory, it is typically kept in storage trailers or leased off-site premises. One control issue is the loss of inventory in these locations, because access to the inventory may be less secure than in the main corporate warehouse. Another problem is the accuracy of inventory records in the off-site locations. Both control issues are dealt with through the following controls: Loss: Access control. When seasonal demand forces inventory levels higher than the storage capacity of the main warehouse area, overflow stocks must be stored elsewhere, possibly in locations having less restrictive access controls. Consider as the best alternative the use of a third-party warehouse with full access controls. If not available, at least lock down access to any additional rented space. If storage trailers are used for overflow storage, be aware that an entire trailer can easily be stolen, so fence off all storage trailers and lock the gate. Accuracy: Include off-site inventory counts in the closing procedure. A common problem is not including in the month-end inventory the inventory counts for off-site storage locations, resulting in an excessively large charge to the cost

Choices to be made with regard to genetic algorithms are largely down to the parameter settings and selection of operators, as described in previous sections. The user also needs to determine whether a steady-state or generational approach is taken. The steady-state approach is faster, as fewer objective function evaluations have to be completed per iteration, but the results may not be as good as a generational run. Multi-objective genetic algorithms can be separated into two distinct types, the elitist and non-elitist algorithms. Elitism ensures that the very best solutions in one generation progress to the next, a concept which does not normally occur as the selection procedure can choose to select any solution from the population. Elitism is useful for multi-objective problems as it helps to preserve the Pareto-front during the optimization. Elitist genetic algorithms such as the Nondominated Sorting Genetic Algorithm 2 (NSGA-II) (Deb et al., 2000) are currently the state-of-the-art and to a certain extent has superseded the original Multi-objective Genetic Algorithm (MOGA) developed by Fonseca and Fleming (1995).

The discussion in the last section on different B2B models should have made it clear of the necessity for integration between corporations and their different systems as well as the necessity for transmission of information electronically. This integration needs to address the following:

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Figure 14.29 IV characteristics of diamond high voltage diode, after [173]. Shown are three measurements of contacts in air and oil. The breakdown of the contacts in air was caused by arcing. Reprinted from Semicond. Sci. Technol., Exceptionally high voltage Shottky diamond diodes and low boron doping by J. Butler, M. Geis, K. Krohn, J. Lawless, S. Deneault, T. Lyszczarz, D. Flechtner and R. Wright,18 S67, Copyright (2003) Institute of Physics

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