Application of GATS, a hybrid meta-heuristic model of genetic algorithm and tabu search, to solve a real-size problem of flow-shop scheduling with changeover times in operations: A case study in the flushing-kit-manufacturing industry

Abstract

Flow shop scheduling (FSS) problems are nondeterministic polynomial (NP)-hard combinatorial optimization problems. It is quite difficult to achieve an optimal solution for real-size problems with mathematical modelling approaches. Meta-heuristics algorithms, like genetic algorithm (GA) and tabu search (TS), play a major role in searching for near-optimal solutions for NP-hard optimization problems. The scheduling method in the case study is not effective; the total tardiness time of orders is rather high. This paper develops a genetic algorithm and tabu search (GATS) algorithm for solving the real FSS problem, with the objective to schedule orders more effectively than the current earliest due date (EDD) model. The GATS algorithm is a hybrid meta-heuristic model, combining GA and TS. In the model, GA is used as the platform for global search, and TS is used to support GA in local search. The performance of the algorithm is compared with the heuristic EDD model being used. The result shows that the algorithm is a good approach for FSS problems. However, the factors of the algorithm are chosen empirically, so the results are only better than the results of the current approach, and not really satisfactory. Future research is to use the experimental design to identify the algorithm’s factors to obtain better results.