SUMMARY
Flow-shop scheduling problems constitute a type of problem that is frequently discussed in the literature where a wide variety of methods are developed for their solution. Although the problem used to be set as a single purpose, it became necessary to expect more than one objective to be evaluated together with increasing customer expectation and competition, after which studies were started to be carried out under the title of multi-objective flow-shop scheduling. With the increase in the number of workbenches and jobs, the difficulty level of the problem increases in a nonlinear way, and the solution becomes more difficult. This study proposes a new hybrid algorithm by combining genetic algorithms, which are metaheuristic methods, and the MultiMoora method, which is a multi-criterion decision-making method, for the solution of multi-objective flow-shop scheduling problems. The study evaluates and tries to optimize the performance criteria of maximum completion time, average flow time, maximum late finishing, average tardiness and the number of late (tardy) jobs. The proposed algorithm is compared to the standard multi-objective genetic algorithm (MOGA) and the MultiMoora-based genetic algorithm (MBGA) shows better results.