A heuristic algorithm for mean flowtime objective in flowshop scheduling
Computers and Operations Research
Solving the Flow Shop Problem by Parallel Tabu Search
PARELEC '02 Proceedings of the International Conference on Parallel Computing in Electrical Engineering
Comparison of heuristics for flowtime minimisation in permutation flowshops
Computers and Operations Research
A combinatorial particle swarm optimisation for solving permutation flowshop problems
Computers and Industrial Engineering
Computers and Operations Research
Computers and Operations Research
Solving the flow shop problem by parallel programming
Journal of Parallel and Distributed Computing
Parallel scatter search algorithm for the flow shop sequencing problem
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
Tabu Search with two approaches to parallel flowshop evaluation on CUDA platform
Journal of Parallel and Distributed Computing
Hybridizing VNS and path-relinking on a particle swarm framework to minimize total flowtime
Expert Systems with Applications: An International Journal
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In this paper, parallelisable Simulated Annealing with Genetic Enhancement (SAwGE) algorithm is presented and applied to Permutation Flowshop Scheduling Problem with total flowtime criterion. This problem is proved to be NP-complete in a strong sense for more than one machine. SAwGE is based on a Clustering Algorithm for Simulated Annealing (SA), but introduces a new mechanism for dynamic SA parameters adjustment, based on genetic algorithms. Computational experiments, based on 120 benchmark datasets by Taillard, show that SAwGE outperforms other heuristics and metaheuristics presented recently in literature. Moreover SAwGE obtains 118 best solutions, including 81 newly discovered ones.