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Lexicographic optimization‐based clustering search metaheuristic for the multiobjective flexible job shop scheduling problem
Author(s) -
Bissoli Dayan C.,
Zufferey Nicolas,
Amaral André R. S.
Publication year - 2021
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.12745
Subject(s) - job shop scheduling , computer science , mathematical optimization , metaheuristic , workload , job shop , lexicographical order , scheduling (production processes) , cluster analysis , multi objective optimization , set (abstract data type) , flow shop scheduling , artificial intelligence , mathematics , machine learning , schedule , combinatorics , programming language , operating system
Abstract In recent years, the flexible job shop scheduling problem (FJSP) has received a great deal of attention from researchers not only due to its complexity but also due to its wide range of applications in the industry. The FJSP extends the job shop scheduling problem (JSP) by allowing operations to be processed by a set of alternative machines. Many of the studies found in the literature consider the objective of minimizing the largest completion time of the jobs, that is, the makespan . However, in the real context of industries, considering more than one criterion is often relevant. Thus, the present work addresses two additional criteria besides the makespan: minimizing the maximum workload of the machines and minimizing the total workload of the machines. Aiming at real cases, where it is necessary to define priorities among the criteria, a clustering search (CS) algorithm was implemented using a lexicographic classification of the objectives for solving the multiobjective FJSP (MOFJSP). The results of this study show that compared to the state‐of‐the‐art approach, CS is an effective alternative to solve the MOFJSP.