Abstract
We face the job-shop problem with operators and total flow time minimization. This problem extends the classical job-shop problem by considering a limited number of operators that assist the processing of the operations. We propose a schedule generation scheme that extends the well-known G&T algorithm. This scheme is then exploited to design an any-time algorithm that combines best-first and greedy search and takes profit from two monotonic heuristics and a method for pruning states based on dominance relations. The results of an experimental study across several benchmarks show that our approach outperforms a constraint programming approach.
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Sierra, M.R., Mencía, C., Varela, R. (2011). Optimally Scheduling a Job-Shop with Operators and Total Flow Time Minimization. In: Lozano, J.A., Gámez, J.A., Moreno, J.A. (eds) Advances in Artificial Intelligence. CAEPIA 2011. Lecture Notes in Computer Science(), vol 7023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25274-7_20
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DOI: https://doi.org/10.1007/978-3-642-25274-7_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-25273-0
Online ISBN: 978-3-642-25274-7
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