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Acquisition of approximate throughput formulas for serial production lines with parallel machines using intelligent techniques
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, University of the Aegean, Chios, Greece.
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, University of the Aegean, Chios, Greece.ORCID iD: 0000-0002-1319-513X
Management and Decision Engineering Laboratory, Department of Financial and Management Engineering, University of the Aegean, Chios, Greece.
2018 (English)In: SETN '18: Proceedings of the 10th Hellenic Conference on Artificial Intelligence, ACM Digital Library, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Estimating the performance of a production line is a difficult problem because of the enormous number of states that exist when analyzing such systems. In addition to the methods developed to address the problem, it is very useful to have a formula linking the characteristics of the line to its performance. Three cases of sort serial production lines with parallel and identical machines in each workstation are examined in this paper. By using a combinational method that applies genetic programming (GP) and an innovative nature inspired method, named sonar inspired optimization (SIO) to improve the results, three models are derived to obtain the throughput of the corresponding lines. Further work will take place because results derived in this paper are encouraging.

Place, publisher, year, edition, pages
ACM Digital Library, 2018.
Series
ACM International Conference Proceeding Series
Keywords [en]
Genetic programming, Parallel-machine stations, Performance evaluation, Serial production lines, Sonar Inspired Optimization, Artificial intelligence, Genetic algorithms, Sonar, Combinational methods, Intelligent techniques, Number of state, Parallel and identical machines, Parallel machine, Performance evaluations, Production line, Serial production line, Throughput
National Category
Production Engineering, Human Work Science and Ergonomics Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-63839DOI: 10.1145/3200947.3201028Scopus ID: 2-s2.0-85052012683ISBN: 9781450364331 (print)OAI: oai:DiVA.org:hj-63839DiVA, id: diva2:1845883
Conference
10th Hellenic Conference on Artificial Intelligence, SETN 2018, 9-12 July 2018
Available from: 2024-03-20 Created: 2024-03-20 Last updated: 2024-03-20Bibliographically approved

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Tzanetos, Alexandros

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CiteExportLink to record
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Citation style
  • apa
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