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for problem solving but also new representation formalisms which allow the explicit representation and use of the knowledge of the domain, mainly by rule-based and constraint-based representations of scheduling knowledge The investigated techniques are as follows (Dorn and Froeschl, 1993; Kempf, 1989; Sauer, 2000; Smith, 1992; Tate, 1996; Zweben and Fox, 1994): Heuristics Heuristic scheduling is based on heuristic search techniques and general heuristics such as problem decomposition together with problem speci c knowledge adopted from scheduling experts in order to guide the search process Often, constraints are used to guide the search Knowledge representation is rule- or frame-based (object-oriented) Important strategies are orderbased, resource-based, or operation-based decompositions together with additional heuristic rules for solving the subproblems (eg, selecting the right resource out of alternatives) Constraints.

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In constraint-based scheduling, the scheduling problem is de ned as a constraint satisfaction problem, that is, the problem is described by a set of variables of certain domains and a set of constraints restricting these domains (Kumar, 1992) For the solution of problems presented as constraint nets, ef cient systems have been developed implementing fast constraint handling algorithms for example, ECLIPSE, CHIP, or ILOG (Baptiste et al, 1995; Dincbas et al, 1989) Problems exist with the handling of hard and soft constraints as well as with reactive scheduling tasks Iterative Improvement Strategies In contrast to the other techniques which mainly construct a solution (schedule), iterative improvement techniques are working on complete solutions The improvement techniques look at scheduling as a combinatorial optimization problem, start with any solution, and try to nd an optimal or near-optimal solution by iterative improvements (Dorn, 1995).

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This means they generate new solutions and check whether the objective value has improved Genetic algorithms, simulated annealing, taboo search, threshold acception, grand deluge algorithms, and iterative deepening belong to this class of strategies Fuzzy Techniques Fuzzy scheduling provides the possibility to deal with the inherent dynamic and incompleteness of the scheduling area It allows the representation (by fuzzy sets and linguistic variables) and the inference (by fuzzy rules) from vaguely formulated knowledge (Slany, 1996; Turksen, 1991) The main types of imprecise scheduling information addressed by fuzzy sets are vaguely de ned dates or durations (eg, due dates), vague de nitions of preferences (eg, preferences between alternatives), uncertainty about the value of scheduling parameters (eg, process times), and aggregated knowledge (eg, machine groups instead of individual machines) Fuzzy controllers are used to implement the handling of the fuzzy rules and fuzzy sets.

Since in this case all the constitutive relations (eq. (1.2)) of the elements are linear and one of them is differential, the system (eq. (1.3)) turns out to be a linear differential

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Thus fuzzy scheduling may be interpreted as a special form of heuristic rule-based system Neural Networks Neural networks too have been investigated for solving scheduling problems Neural networks solve complex functions represented.

A major difference is that DP solves the problem of optimality by using Bellman s principle of optimal subsolutions The multiple-period heuristic solves more or less greedy We stipulated that the complexity of the shunting problem makes it computationally infeasible to apply an exact algorithm like DP Therefore one is forced to apply an approximation algorithm, like the multiple-period heuristic The Dynamic Programming technique inspired us to develop this heuristic in which the number of decisions and the number of states is less explosive The multiple-period heuristic evaluates many decisions from which it selects the best one, but it ends up with only one state at each stage Hence the multiple-period heuristic tries to nd a good solution by drawing a low-cost path through the DP graph In cases where the heuristic gets stuck at a state, it needs to retrace in the DP graph.

Table 9.1 Comparison of predicted and measured NOX and unburned carbon loss for Mitsui Babcock 40 MW rig, burning South Brandon