Fast Multi-objective Scheduling of Jobs to Constrained Resources Using a Hybrid Evolutionary Algorithm

Authors:
Wilfried Jakob;Alexander Quinte;Karl-Uwe Stucky;Wolfgang Süß
Affiliations:
Forschungszentrum Karlsruhe GmbH, Institute for Applied Computer Science, Karlsruhe, Germany 76021;Forschungszentrum Karlsruhe GmbH, Institute for Applied Computer Science, Karlsruhe, Germany 76021;Forschungszentrum Karlsruhe GmbH, Institute for Applied Computer Science, Karlsruhe, Germany 76021;Forschungszentrum Karlsruhe GmbH, Institute for Applied Computer Science, Karlsruhe, Germany 76021
Venue:
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Year:
2008

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Abstract

The problem tackled here combines three properties of scheduling tasks, each of which makes the basic task more challenging: job scheduling with precedence rules, co-allocation of restricted resources of different performances and costs, and a multi-objective fitness function. As the algorithm must come up with results within a few minutes runtime, EA techniques must be tuned to this limitation. The paper describes how this was achieved and compares the results with a common scheduling algorithm, the Giffler-Thompson procedure.