Abstract
This paper studies an assembly flow shop scheduling problem with three stages. At the first stage, m parts of a job are independently processed by m parallel machines. At the second stage, the manufactured parts are collected and transferred to the assembly stage where the parts are assembled into the final product. In this problem sequence-dependent setup times are considered at each stage, and the objective is to minimize the total tardiness. Due to the computational complexity of the problem and the limitations of exact approaches to find optimal solutions for large size instances, we propose a hybrid heuristic based on the meta-heuristics Iterated Greedy (IG) and Iterated Local Search (ILS). The performance of the heuristic is compared with a General Variable Neighborhood Search algorithm, proposed previously. The computational results show that our heuristic presents a superior performance compared to other heuristics.
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