A new approach to solving multiobjective flowshop scheduling problems: A multi-MOORA-based genetic algorithm

Abstract

Flowshop 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 started to be carried out under the title of multiobjective flowshop 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 Multi-MOORA method, which is a multicriterion decision-making method, for the solution of multiobjective flowshop 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 multiobjective genetic algorithm (MOGA), and the Multi-MOORA-based genetic algorithm (MBGA) shows better results.