Repairing infeasibility in scheduling via genetic algorithms

  1. Raúl Mencía
  2. Carlos Mencía
  3. Ramiro Varela
Libro:
From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II
  1. José Manuel Ferrández Vicente (dir. congr.)
  2. José Ramón Álvarez-Sánchez (dir. congr.)
  3. Félix de la Paz López (dir. congr.)
  4. Javier Toledo Moreo (dir. congr.)
  5. Hojjat Adeli (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-19651-6

Ano de publicación: 2019

Páxinas: 254-263

Tipo: Capítulo de libro

DIALNET GOOGLE SCHOLAR

Resumo

Scheduling problems arise in an ever increasing number ofapplication 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 searchspace defined by an efficient solution builder, also proposed in the paper. Experimental results show the suitability of our approach.

Fonte de datos: Dialnet