att_abstract={{This paper addresses the Permutation Flowshop Problem with minimization of
makespan, which is denoted by F|prmu|Cmax. In the permutational
scenario, the sequence of jobs has to remain the same in all machines. The
Flowshop Problem (FSP) is known to be NP-hard when more than three
machines are considered. Thus, for medium and large scale instances,
high-quality heuristics are needed to find good solutions in reasonable
time.  We propose and analyse parallel hybrid search methods that fully 
use the computational power of current multi-core machines. The parallel 
methods combine a memetic algorithm (MA) and several iterated greedy
algorithms (IG) running concurrently. Two test scenarios were included,
with short and long CPU times. The tests were conducted on the set of 
benchmark instances introduced by Taillard in 1993, commonly used to
assess the performance of new methods.  Results indicate that the use
of the MA to manage a pool of solutions is highly effective, allowing 
the improvement of the best known upper bound for one of the instances.
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	att_categories={C_CCF.1, C_CCF.2, C_CCF.7, C_CCF.8},
	att_copyright_notice={{The definitive version was published in Annals of Operations Research (Springer). {{, 2011-12-31}}{{, http://rd.springer.com/article/10.1007%2Fs10479-011-1056-3}} }},
	att_tags={Metaheursitics, memetic algorithms, flowshop problem, combinatorial optimization, scheduling},
	author={Mauricio Resende and Martin G. Ravetti, Fed. U. of Minas Gerais and Carlos Riveros, U. of Newcastle and Alexandre Mendes, U. of Newcastle and Panos M. Pardalos},
	institution={{Annals of Operations Research}},
	title={{Parallel hybrid heuristics for the permutation flow shop problem}},