Optimally scheduling a job-shop with operators and total flow time minimization

Authors:
María R. Sierra;Carlos Mencía;Ramiro Varela
Affiliations:
Department of Computer Science, University of Oviedo, Gijón, Spain;Department of Computer Science, University of Oviedo, Gijón, Spain;Department of Computer Science, University of Oviedo, Gijón, Spain
Venue:
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Year:
2011

Citing 1
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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.