In this paper the job shop scheduling problem with two criteria of minimizing makespan and the sum of tardiness of jobs is considered. This multi-objective problem is strongly NP-hard, as single criterion version is strongly NP-hard as well. A permutation-based representation for the job shop problem is used and a new hybrid parallel multi-agent method, called GACO (Genetic Algorithm Ant Colony Optimization), is proposed. The computation is done in parallel and additional threads concurrently compute certain parts of both algorithms. The researched speed-up is considerable, albeit limited by the need to combine solutions. Approximation of the Pareto front obtained by GACO is superior to the approximations obtained by GA and ACO separately.
CITATION STYLE
Rudy, J., & Żelazny, D. (2015). GACO: A parallel evolutionary approach to multi-objective scheduling. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9018, pp. 307–320). Springer Verlag. https://doi.org/10.1007/978-3-319-15934-8_21
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