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A biased‐randomized iterated local search for the distributed assembly permutation flow‐shop problem
Author(s) -
Ferone Daniele,
Hatami Sara,
GonzálezNeira Eliana M.,
Juan Angel A.,
Festa Paola
Publication year - 2020
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12719
Subject(s) - iterated local search , flow shop scheduling , job shop scheduling , iterated function , computer science , scheduling (production processes) , mathematical optimization , permutation (music) , metaheuristic , local search (optimization) , product (mathematics) , set (abstract data type) , flow (mathematics) , algorithm , mathematics , routing (electronic design automation) , computer network , mathematical analysis , physics , geometry , acoustics , programming language
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.