A biased-randomized iterated local search for the distributed assembly permutation flow-shop problem

Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow-shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow-shop configuration; in the second stage, the outcomes of each flow-shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter-less algorithm that makes use of a biased-randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state-of-the-art approaches, obtaining several new best solutions.