thesis
Evolutionary methods for the design of dispatching rules for complex and dynamic scheduling problems
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Abstract
Three methods, based on Evolutionary Algorithms (EAs), to support and automate the design
of dispatching rules for complex and dynamic scheduling problems are proposed in this thesis.
The first method employs an EA to search for problem instances on which a given dispatching
rule performs badly. These instances can then be analysed to reveal weaknesses of the
tested rule, thereby providing guidelines for the design of a better rule. The other two methods
are hyper-heuristics, which employ an EA directly to generate effective dispatching rules. In
particular, one hyper-heuristic is based on a specific type of EA, called Genetic Programming
(GP), and generates a single rule from basic job and machine attributes, while the other generates
a set of work centre-specific rules by selecting a (potentially) different rule for each
work centre from a number of existing rules. Each of the three methods is applied to some
complex and dynamic scheduling problem(s), and the resulting dispatching rules are tested
against benchmark rules from the literature. In each case, the benchmark rules are shown to be
outperformed by a rule (set) that results from the application of the respective method, which
demonstrates the effectiveness of the proposed methods