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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Repairing Infeasibility in Scheduling via Genetic Algorithms
verfasst von : Raúl Mencía, Carlos Mencía, Ramiro Varela
Erschienen in: From Bioinspired Systems and Biomedical Applications to Machine Learning
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Abstract
Scheduling problems arise in an ever increasing number of application domains. Although efficient algorithms exist for a variety of such problems, sometimes it is necessary to satisfy hard constraints that make the problem unfeasible. In this situation, identifying possible ways of repairing infeasibility represents a task of utmost interest. We consider this scenario in the context of job shop scheduling with a hard makespan constraint and address the problem of finding the largest possible subset of the jobs that can be scheduled within such constraint. To this aim, we develop a genetic algorithm that looks for solutions in the search space defined by an efficient solution builder, also proposed in the paper. Experimental results show the suitability of our approach.