Santos, Haroldo GambiniToffolo, Túlio Ângelo MachadoSilva, Cristiano Luís Turbino de França eBerghe, Greet Vanden2017-02-012017-02-012016SANTOS, H. G. et al. Analysis of stochastic local search methods for the unrelatedparallel machine scheduling problem. International Transactions in Operational Research, v. 26, p. 707-724, 2016. Disponível em: <http://onlinelibrary.wiley.com/doi/10.1111/itor.12316/epdf>. Acesso em: 20 jan. 2017.1475-3995http://www.repositorio.ufop.br/handle/123456789/7171This work addresses the unrelated parallel machine scheduling problem with sequence-dependent setup times,in which a set of jobs must be scheduled for execution by one of the several available machines. Each jobhas a machine-dependent processing time. Furthermore, given multiple jobs, there are additional setup times,which vary based on the sequence and machine employed. The objective is to minimiz e the schedule’s com-pletion time (makespan). The problem is NP-hard and of significant practical relevance. The present paperinvestigates the performance of four different stochastic local search (SLS) methods designed for solvingthe particular scheduling problem: simulated annealing, iterated local search, late acceptance hill-climbing,and step counting hill-climbing. The analysis focuses on design questions, tuning effort, and optimizationperformance. Simple neighborhood structures are considered. All proposed SLS methods performed signifi-cantly better than two state-of-the-art hybrid heuristics, especially for larger instances. Updated best-knownsolutions were generated for 901 of the 1000 large benchmark instances considered, demonstrating that par-ticular SLS methods are simple yet powerful alternatives to current approaches for addressing the problem.Implementations of the contributed algorithms have been made available to the research community.en-USrestritoHeuristicsMetaheuristicsArtigo publicado em periodicohttp://onlinelibrary.wiley.com/doi/10.1111/itor.12316/epdfhttps://doi.org/10.1111/itor.12316