A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem

Recently, the flowshop scheduling problem to minimize total flowtime has attracted more attention from researchers. In this paper, a genetic local search algorithm is proposed to solve this problem. The proposed algorithm hybridizes the genetic algorithm and the tabu search. It employs the genetic algorithm to do the global search and the tabu search to do the local search. The orthogonal-array-based crossover is utilized to enhance the capability of intensification. Also, a novel orthogonal-array-based mutation is proposed, in order to add capability of intensification to the traditional mutation operator. The performance of the proposed genetic local search algorithm is very competitive. It improved 54 out of 90 current best solutions reported in the literature for short-term search, and it also improved 18 out of 20 current best solutions reported in the literature for long-term search.