Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time

Abstract

This article addresses the two-stage assembly flow shop scheduling problem with release time of jobs which is applicable in many industrial areas, such as computer manufacturing industry, fire engine assembly plant, etc. The first stage of the problem is called “fabrication stage” and includes identical parallel machines while the second stage is called “assembly stage” with a single assembly machine. The jobs have components which they need to be firstly processed at the fabrication stage and then they should go under assembly operation at the assembly stage. The goal of this research is to find the jobs sequence such that completion time of the last processed job is minimized. For this problem, several heuristic techniques as well as a lower bound are developed. Also, a novel meta-heuristic algorithm called Grey Wolf Optimizer (GWO), which is inspired by living and haunting behavior of wolves, is then proposed. An extensive statistical analysis is conducted to compare the performance of the employed algorithms on randomly generated instances. The obtained results indicate that the methods based on Johnson’s algorithm yield better results than the other heuristic algorithms. Moreover, the consequences show that the proposed LB is tight enough. Finally, the experiments show that the GWO outperforms the other employed well-known meta-heuristic algorithms.