Using variable neighbourhood descent and genetic algorithms for sequencing mixed-model assembly systems in the footwear industry
Parisa Sadeghi, Rui Diogo Rebelo, José Soeiro Ferreira
This paper addresses a new Mixed-model Assembly Line Sequencing Problem in the Footwear industry. This problem emerges in a large company, which benefits from advanced automated stitching systems. However, these systems need to be managed and optimised. Operators with varied abilities operate machines of various types, placed throughout the stitching lines. In different quantities, the components of the various shoe models, placed in boxes, move along the lines in either direction. The work assumes that the associated balancing problems have already been solved, thus solely concentrating on the sequencing procedures to minimise the makespan. An optimisation model is presented, but it has just been useful to structure the problems and test small instances due to the practical problems’ complexity and dimension. Consequently, two methods were developed, one based on Variable Neighbourhood Descent, named VND-MSeq, and the other based on Genetic Algorithms, referred to as GA-MSeq. Computational results are included, referring to diverse instances and real large-size problems. These results allow for a comparison of the novel methods and to ascertain their effectiveness. We obtained better solutions than those available in the company.
Year of publication: |
2021
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Authors: | Sadeghi, Parisa ; Diogo Rebelo, Rui ; Soeiro Ferreira, José |
Published in: |
Operations research perspectives. - Amsterdam [u.a.] : Elsevier, ISSN 2214-7160, ZDB-ID 2821932-6. - Vol. 8.2021, Art.-No. 100193, p. 1-19
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Subject: | Mixed-model assembly line sequencing problem | Variable neighbourhood descent | Genetic algorithms | Dispatching rules | Evolutionärer Algorithmus | Evolutionary algorithm | Scheduling-Verfahren | Scheduling problem | Fließfertigung | Assembly-line production |
Saved in:
freely available
Type of publication: | Article |
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Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
Language: | English |
Other identifiers: | 10.1016/j.orp.2021.100193 [DOI] hdl:10419/246450 [Handle] |
Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10012596628
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