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7.2. PROBLEM EXPLORATION In the previous section, we have stated that not much has changed with respect to the basic problems in applying interactive scheduling systems. In this section, we will investigate the causes of these problems in detail in two parts. First, we will provide an overview of functionality that is commonly found in interactive scheduling systems. Second, three related causes of the gap between theory and practice will be discussed. 7.2.1. Basic Components of Interactive Scheduling Systems The amount of information that is involved in planning problems makes it necessary to use external tools for information storage or manipulation. Pen and paper ( the backside of a cigar box ) or a planning board are still no exception, but computerized support is common nowadays. In practice, word processors and spreadsheets are still frequently used to support planning and scheduling, although several authors note that such general programs do not suf ce for even slightly complex planning situations (Green, 1996; Kondili et al., 1993; Jakeman, 1994). Literature shows variety in functionality that is applied for scheduling systems. The following scheduling system characteristics can be distinguished: 1. Components. Interactive scheduling systems are basically like any other kind of decision support systems consisting of a database, a user interface, a model base with algorithms, and links to external systems. The database stores all information, the user interface shows the stored information and provides possibilities to update and manipulate the information, and the model base contains generation algorithms. Scheduling speci c functionalities are a constraint checker, a goal evaluator, and a blackboard where multiple solutions and partial solutions can be stored for later retrieval. Figure 7.1 shows a

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Thus, Hk = 0 for k = 0, 1, . . . , n if (x) is orthogonal to Lk (x) over [a, b] with respect to the weighting function w(x). Since deg(Lk (x)) = n (all k), this will be the case if (x) is orthogonal to all polynomials of degree n over [a, b] with respect to the weighting function w(x). Note that deg( (x)) = n + 1 [recall (9.69)]. In fact, the polynomial (x) of degree n + 1 is orthogonal to all polynomials of degree n over [a, b] with respect to w(x), the Hermite quadrature formula reduces to the simpler form

Figure 10-43: I captured this youthful fellow with a low-end digital camera equipped with a removable fish-eye lens. How innocent and happy he looks obviously not a computer user.

and off times, and an on off model with Pareto-distributed on and off times which can generate traffic with long-range dependence . Recent studies w85, 87, 88x have shown that network traffic often exhibits long-range dependence LRD, with the implications that congested periods can be quite long and a slight increase in the number of active connections can result in large increases in the packet loss rate w86x Interested readers are referred to w48, 81x for the simulation details The simulation results w48, 81x show that the acceptance-region-based algorithm w44x is either too conservative when prr is large, or too optimistic when the flows have heavy-tailed on- and off-time distributions.

Since only individual measurements are available, moving ranges need to be calculated for the estimation of process standard deviation . A k-point moving range, MRk, can be calculated as MR k = max (xi , . . . , xi + k ) min ( xi , . . . , xi + k ) (15)

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