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Sensors 2019, 19(2), 297; https://doi.org/10.3390/s19020297

Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing

1
Department of Information Technology, Ghent University/IMEC, Technologiepark 126, 9052 Ghent, Belgium
2
Huawei Technologies, Songshan Lake Technology Park, Dongguan 523808, China
*
Author to whom correspondence should be addressed.
Received: 29 November 2018 / Revised: 4 January 2019 / Accepted: 8 January 2019 / Published: 13 January 2019
(This article belongs to the Section Sensor Networks)
PDF [1057 KB, uploaded 13 January 2019]

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: genetic algorithm; continuous production scheduling; energy and failure management; pasta manufacturing genetic algorithm; continuous production scheduling; energy and failure management; pasta manufacturing
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Shen, K.; Pessemier, T.D.; Gong, X.; Martens, L.; Joseph, W. Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing. Sensors 2019, 19, 297.

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