{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T08:48:17Z","timestamp":1774514897917,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,13]],"date-time":"2019-01-13T00:00:00Z","timestamp":1547337600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19020297","type":"journal-article","created":{"date-parts":[[2019,1,14]],"date-time":"2019-01-14T12:20:07Z","timestamp":1547468407000},"page":"297","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Genetic Optimization of Energy- and Failure-Aware Continuous Production Scheduling in Pasta Manufacturing"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2502-7088","authenticated-orcid":false,"given":"Ke","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Information Technology, Ghent University\/IMEC, Technologiepark 126, 9052 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Toon","family":"De Pessemier","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Ghent University\/IMEC, Technologiepark 126, 9052 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7568-9645","authenticated-orcid":false,"given":"Xu","family":"Gong","sequence":"additional","affiliation":[{"name":"Huawei Technologies, Songshan Lake Technology Park, Dongguan 523808, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Martens","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Ghent University\/IMEC, Technologiepark 126, 9052 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8807-0673","authenticated-orcid":false,"given":"Wout","family":"Joseph","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Ghent University\/IMEC, Technologiepark 126, 9052 Ghent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.compchemeng.2015.02.004","article-title":"Optimization of steel production scheduling with complex time-sensitive electricity cost","volume":"76","author":"Hadera","year":"2015","journal-title":"Comput. 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