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Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing

Ke Shen (UGent) , Toon De Pessemier (UGent) , Xu Gong (UGent) , Luc Martens (UGent) and Wout Joseph (UGent)
(2019) SENSORS. 19(2).
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Abstract
Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.
Keywords
UNCERTAINTY, CONSUMPTION, ALGORITHMS, SIMULATION, MACHINE, genetic algorithm, continuous production scheduling, energy and failure, management, pasta manufacturing

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Citation

Please use this url to cite or link to this publication:

Chicago
Shen, Ke, Toon De Pessemier, Xu Gong, Luc Martens, and Wout Joseph. 2019. “Genetic Optimization of Energy- and Failure-aware Continuous Production Scheduling in Pasta Manufacturing.” Sensors 19 (2).
APA
Shen, K., De Pessemier, T., Gong, X., Martens, L., & Joseph, W. (2019). Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing. SENSORS, 19(2).
Vancouver
1.
Shen K, De Pessemier T, Gong X, Martens L, Joseph W. Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing. SENSORS. Basel: Mdpi; 2019;19(2).
MLA
Shen, Ke et al. “Genetic Optimization of Energy- and Failure-aware Continuous Production Scheduling in Pasta Manufacturing.” SENSORS 19.2 (2019): n. pag. Print.
@article{8604168,
  abstract     = {Energy and failure are separately managed in scheduling problems despite the commonalities between these optimization problems. In this paper, an energy- and failure-aware continuous production scheduling problem (EFACPS) at the unit process level is investigated, starting from the construction of a centralized combinatorial optimization model combining energy saving and failure reduction. Traditional deterministic scheduling methods are difficult to rapidly acquire an optimal or near-optimal schedule in the face of frequent machine failures. An improved genetic algorithm (IGA) using a customized microbial genetic evolution strategy is proposed to solve the EFACPS problem. The IGA is integrated with three features: Memory search, problem-based randomization, and result evaluation. Based on real production cases from Soubry N.V., a large pasta manufacturer in Belgium, Monte Carlo simulations (MCS) are carried out to compare the performance of IGA with a conventional genetic algorithm (CGA) and a baseline random choice algorithm (RCA). Simulation results demonstrate a good performance of IGA and the feasibility to apply it to EFACPS problems. Large-scale experiments are further conducted to validate the effectiveness of IGA.},
  articleno    = {297},
  author       = {Shen, Ke and De Pessemier, Toon and Gong, Xu and Martens, Luc and Joseph, Wout},
  issn         = {1424-8220},
  journal      = {SENSORS},
  language     = {eng},
  number       = {2},
  pages        = {24},
  publisher    = {Mdpi},
  title        = {Genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing},
  url          = {http://dx.doi.org/10.3390/s19020297},
  volume       = {19},
  year         = {2019},
}

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