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An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things

Ke Shen (UGent) , Joachim David, Toon De Pessemier (UGent) , Luc Martens (UGent) and Wout Joseph (UGent)
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Abstract
Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule.
Keywords
batch processing (industrial), continuous production, cost reduction, food manufacturing, genetic algorithms, Internet of Things, scheduling, continuous manufacturing, multiobjective optimization model, genetic algorithm, pasta manufacturer, multiobjective continuous production scheduling, batch processing scenarios, industrial Internet of Things, cost reduction, nonrandom initialization, Mathematical model, Job shop scheduling, Genetic algorithms, Optimization, Continuous production, production scheduling, genetic algorithm, continuous manufacturing, multi-objective optimization, industrial internet of things

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MLA
Shen, Ke, et al. “An Efficient Genetic Method for Multi-Objective Continuous Production Scheduling in Industrial Internet of Things.” 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), IEEE, 2019, pp. 1119–26, doi:10.1109/ETFA.2019.8869049.
APA
Shen, K., David, J., De Pessemier, T., Martens, L., & Joseph, W. (2019). An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things. In 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA) (pp. 1119–1126). Zaragoza, Spain: IEEE. https://doi.org/10.1109/ETFA.2019.8869049
Chicago author-date
Shen, Ke, Joachim David, Toon De Pessemier, Luc Martens, and Wout Joseph. 2019. “An Efficient Genetic Method for Multi-Objective Continuous Production Scheduling in Industrial Internet of Things.” In 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 1119–26. IEEE. https://doi.org/10.1109/ETFA.2019.8869049.
Chicago author-date (all authors)
Shen, Ke, Joachim David, Toon De Pessemier, Luc Martens, and Wout Joseph. 2019. “An Efficient Genetic Method for Multi-Objective Continuous Production Scheduling in Industrial Internet of Things.” In 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), 1119–1126. IEEE. doi:10.1109/ETFA.2019.8869049.
Vancouver
1.
Shen K, David J, De Pessemier T, Martens L, Joseph W. An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things. In: 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA). IEEE; 2019. p. 1119–26.
IEEE
[1]
K. Shen, J. David, T. De Pessemier, L. Martens, and W. Joseph, “An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things,” in 2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA), Zaragoza, Spain, 2019, pp. 1119–1126.
@inproceedings{8636349,
  abstract     = {{Continuous manufacturing is playing an increasingly important role in modern industry, while research on production scheduling mainly focuses on traditional batch processing scenarios. This paper provides an efficient genetic method to minimize energy cost, failure cost, conversion cost and tardiness cost involved in the continuous manufacturing. With the help of Industrial Internet of Things, a multi-objective optimization model is built based on acquired production and environment data. Compared with a conventional genetic algorithm, non-random initialization and elitist selection were applied in the proposed algorithm for better convergence speed. Problem specific constraints such as due date and precedence are evaluated in each generation. This method was demonstrated in the plant of a pasta manufacturer. In experiments of 71 jobs in a one-month window, near-optimal schedules were found with significant reductions in costs in comparison to the existing original schedule.}},
  author       = {{Shen, Ke and David, Joachim and De Pessemier, Toon and Martens, Luc and Joseph, Wout}},
  booktitle    = {{2019 24TH IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION (ETFA)}},
  isbn         = {{9781728103037}},
  issn         = {{1946-0740}},
  keywords     = {{batch processing (industrial),continuous production,cost reduction,food manufacturing,genetic algorithms,Internet of Things,scheduling,continuous manufacturing,multiobjective optimization model,genetic algorithm,pasta manufacturer,multiobjective continuous production scheduling,batch processing scenarios,industrial Internet of Things,cost reduction,nonrandom initialization,Mathematical model,Job shop scheduling,Genetic algorithms,Optimization,Continuous production,production scheduling,genetic algorithm,continuous manufacturing,multi-objective optimization,industrial internet of things}},
  language     = {{eng}},
  location     = {{Zaragoza, Spain}},
  pages        = {{1119--1126}},
  publisher    = {{IEEE}},
  title        = {{An efficient genetic method for multi-objective continuous production scheduling in industrial internet of things}},
  url          = {{http://dx.doi.org/10.1109/ETFA.2019.8869049}},
  year         = {{2019}},
}

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