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Saving potential resources, measured in t/y fuel oil Case A: Traditional waste management/ waste in MWI Case B: 100% Feedstock recycling (Case C: 100% Mechanical recycling) hypothetic, as beyond means Case D: 40% Mechanical recycling 60% Feedstock recycling Case E: 60% Mechanical recycling 40% Feedstock recycling

, 1996), whereas a selective increase in saturated fat intake may impair insulin action (Maron, Fair and Haskell, 1991; Parker et al, 1993; Mayer et al, 1993; Feskens, Loeber and Kromhout, 1994; Marshall, Bessesen and Hamman, 1997; Mooy et al, 1998; MayerDavis et al, 1997) Numerous studies have shown positive correlations between saturated fat intake and circulating insulin levels (an index of insulin resistance), independently of body fat mass (Maron, Fair and Haskell, 1991; Parker et al, 1993; Mayer et al, 1993; Feskens, Loeber and Kromhout, 1994; Marshall, Bessesen and Hamman, 1997; Mooy et al, 1998; MayerDavis et al, 1997) Moreover, increasing unsaturated fat intake is associated with improved insulin sensitivity (Pelikanova et al, 1989; Borkman et al, 1993; Vessby, Tengblad and Lithell, 1994; Pan et al, 1995), particularly when total fat intake is relatively low (Vessby et al.

sqrt() that returns the square root of the first number exp() that returns e raised to the power of the first number

The abs(x) function takes the absolute value of any integer, long integer, or floating point number:

The question is not whether to apply a DSL to planning and scheduling since such an approach has much advantages The issue is how the domain of a planning and scheduling DSL is de ned As we discussed in the previous section, existing frameworks are either limited in the kinds of models they use (organization, domain, task, support) or in the environment that the models can be applied (manufacturing, staff scheduling, large scale military missions, etc) In other words, we state that it is possible to make a DSL that covers more aspects of planning and scheduling and a broader application scope than the existing approaches In this section, we will describe such a DSL (van Wezel and Jorna, 1999; van Wezel, 2001) 731 Foundations In 1, we argued that planning is always performed hierarchically.

, 2001) The mechanisms linking dietary fat composition to insulin sensitivity are under active investigation and may involve changes in the fatty-acid composition of cell membranes, which is known to in uence insulin signalling (Inokuchi, 2006) In diabetic patients, diets rich in omega-6-polyunsaturated fatty acids improve insulin sensitivity (Summers et al, 2002), whereas foods rich in omega3-fatty acids diets do not (Borkman et al, 1991) These ndings have also been con rmed in healthy, non-diabetic subjects (Borkman et al, 1991; Vessby et al, 2001; Rivellese et al, 1996) Fatty acid composition also has powerful effects on lipoprotein metabolism In the.

In this section, we extend on this notion by stating that hierarchic modeling is the only way to establish comprehensiveness in a planning and scheduling DSL This means that only if the necessary models are hierarchical we can realize a comprehensive applicability scope for all kinds of models that are needed The approaches that were discussed in Tables 71 and 72 are a good indication that this is already being applied in existing approaches Almost all authors describe hierarchical object-oriented frameworks or other hierarchical concepts Since the planning in many organizations is too large or complicated to be handled by one person, it is split up The individual parts are then worked on by different persons; their subplans have to be coordinated and combined, which takes place at a higher hierarchical level.

Summary

In 1 we have stated that the same reasoning holds for the task as performed by an individual Exhaustive search by enumerating all possible solutions is impossible, so a human planner working on a subplan will split the problem again in smaller and more manageable parts This substantiates the need for a hierarchical modeling language for the planning task division and the planning task itself From this, it can be deduced that domain models need hierarchies as well; at each level (level of coordination of subplans, task level, subtask level) it must be clear what exactly the subproblem.