Parallel Simulated Annealing with Genetic Enhancement for flowshop problem with Csum

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.