Application of GATS, a hybrid mega-heuristic model, and DOE, to solve flexible flow shop scheduling problems: a case study

Abstract

Flexible flow shop scheduling (FFSS) is an NP-hard combinatorial optimization problem. Solving this problem using mathematical modeling approaches is very difficult. Mega-heuristic algorithms, such as the genetic algorithm (GA) and tabu search (TS), are powerful tools for finding near-optimal solutions to problems of this type. This paper develops a GATS model by combining GA and TS for solving FFSS problems. In the model, GA is used as the platform for global search, and TS is used to support GA in local search. This paper also uses the design of experiments (DOE) to optimize the parameters of the GATS model. The performance of the models, GATS and GATS with DOE, is compared with traditional heuristics being used. The result indicates that the models are good approaches for FFSS problems.