@techreport{TD:101926,
	att_abstract={  This paper introduces a permutation flowshop scheduling problem with job release dates that maximizes the total payoff. This problem has a stepwise job objective function and it is strongly NP-hard. We propose a mathematical formulation and develop a benchmark instances for this problem. Several heuristic methods are described: four constructive heuristics, three of them found in the literature, and a new one. Besides, we present two hybrid heuristics - an Iterated Local Search, and a warm-start strategy. These heuristics are combined with a Variable Neighborhood Descent method.  Additionaly, upper bounds are computed by applying two different relaxations.  Computational experiments show the merit of the proposed greedy constructive heuristic, which is competitive to the classical NEH heuristic for flowshop scheduling problems, by finding similar solution quality while presenting greater complexity and running times. Furthermore, we found out that ILS heuristic outperforms the warm-start strategy.
},
	att_authors={cd338h},
	att_categories={C_P6},
	att_copyright={Elsevier},
	att_copyright_notice={The definitive version was published in European Journal of Operational Research {{, 2017-12-31}}{{, 10.1016/j.ejor.2017.10.045}}
},
	att_donotupload={},
	att_private={false},
	att_projects={},
	att_tags={Flowshop Scheduling,  Heuristics,  Metaheuristics},
	att_techdoc={true},
	att_techdoc_key={TD:101926},
	att_url={http://web1.research.att.com:81/techdocs_downloads/TD:101926_DS1_2017-11-01T14:46:56.210Z.pdf},
	author={Luciana Pessoa AND Carlos E. Andrade},
	institution={{European Journal of Operational Research}},
	month={December},
	title={{Maximizing the payoff on a flowshop scheduling problem with a stepwise job objective function}},
	year=2017,
}