Solving a two-stage assembly flowshop scheduling problem to minimize the mean tardiness and earliness penalties by three meta-heuristics

This paper presents a new mathematical model for a two-stage assembly flowshop scheduling problem that minimizes the mean tardiness and earliness penalties. This problem is an extension of the assembly flow shop problem with simultaneous operations in the first stage and a single assembly operation in the second stage. The problem is known to be NP-hardness; therefore, we propose three meta-heuristic algorithms, namely variable neighborhood search (VNS), genetic algorithm (GA) and simulated annealing (SA), in order to solve such a hard problem. To tune the parameters of the foregoing algorithms, the Taguchi method is used. A number of test problems are considered and the associated data are generated at random. Furthermore, a comprehensive computational experiment is conducted to compare the performance of the proposed meta-heuristics. The related results and statistical experiments show that the proposed VNS outperforms the proposed GA and SA in terms of both the standard deviation and average percentage error measures.