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Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing
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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359374/ https://www.ncbi.nlm.nih.gov/pubmed/30642119 http://dx.doi.org/10.3390/s19020297 |
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author | Shen, Ke De Pessemier, Toon Gong, Xu Martens, Luc Joseph, Wout |
author_facet | Shen, Ke De Pessemier, Toon Gong, Xu Martens, Luc Joseph, Wout |
author_sort | Shen, Ke |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6359374 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-63593742019-02-06 Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing Shen, Ke De Pessemier, Toon Gong, Xu Martens, Luc Joseph, Wout Sensors (Basel) Article 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. MDPI 2019-01-13 /pmc/articles/PMC6359374/ /pubmed/30642119 http://dx.doi.org/10.3390/s19020297 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Shen, Ke De Pessemier, Toon Gong, Xu Martens, Luc Joseph, Wout Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title | Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title_full | Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title_fullStr | Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title_full_unstemmed | Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title_short | Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing |
title_sort | genetic optimization of energy- and failure-aware continuous production scheduling in pasta manufacturing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6359374/ https://www.ncbi.nlm.nih.gov/pubmed/30642119 http://dx.doi.org/10.3390/s19020297 |
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