A novel hybrid genetic algorithm to solve the make-to-order sequence-dependent flow-shop scheduling problem

Flow-shop scheduling problem (FSP) dealswith the scheduling of a set of n jobs that visit a set ofm machines in the same order. As the FSP is NP-hard, thereis no efficient algorithm to reach the optimal solution of theproblem. To minimize the holding, delay and setup costs oflarge permutation flow-shop scheduling problems withsequence-dependent setup times on each machine, thispaper develops a novel hybrid genetic algorithm (HGA)with three genetic operators. Proposed HGA applies amodified approach to generate a pool of initial solutions,and also uses an improved heuristic called the iterated swap < /div>procedure to improve the initial solutions. We consider themake-to-order production approach that some sequencesbetween jobs are assumed as tabu based on maximumallowable setup cost. In addition, the results are comparedto some recently developed heuristics and computationalexperimental results show that the proposed HGA performsvery competitively with respect to accuracy and efficiencyof solution.