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.
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Acknowledgements
This research is supported by the Spanish Government under project TIN2016-79190-R and by the Principality of Asturias under grant IDI/2018/000176.
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Mencía, R., Mencía, C., Varela, R. (2019). Repairing Infeasibility in Scheduling via Genetic Algorithms. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) From Bioinspired Systems and Biomedical Applications to Machine Learning. IWINAC 2019. Lecture Notes in Computer Science(), vol 11487. Springer, Cham. https://doi.org/10.1007/978-3-030-19651-6_25
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DOI: https://doi.org/10.1007/978-3-030-19651-6_25
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