2017
DOI: 10.14569/ijacsa.2017.081256
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A Firefly Algorithm for the Mono-Processors Hybrid Flow Shop Problem

Abstract: Abstract-Nature-inspired swarm metaheuristics become one of the most powerful methods for optimization. In discrete optimization, the efficiency of an algorithm depends on how it is adapted to the problem. This paper aims to provide a discretization of the Firefly Algorithm (FF) for the scheduling of a specific manufacturing system, which is the mono processors two-stage hybrid flow shop (HFS). This kind of manufacturing system appears in several fields as the operating theatre scheduling problem. Results of p… Show more

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Cited by 3 publications
(2 citation statements)
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“…Many research works were based on this algorithm to solve their problems. FireFly was used to solve the mono-processors hybrid flow shop problem (Dekhici & Belkadi, 2017), flexible operation scheduling (Fuyu, Weining, & Yan, 2018), facial expression recognition (Mistry, Zhang, Sexton, Zeng, & He, 2017), home care scheduling (Dekhici, Redjem, Belkadi, & Mhamedi, 2019), biomedical engineering (BME), healthcare (Nayak, Naik, Dinesh, Vakula, & Byomakesha, 2020), and also for image analysis (Dey, Chaki, Moraru, Fong, & Yang, 2020). Some researchers stated that FA was a powerful algorithm for solving even some of the NP-complete problems (Kumar & Kumar, 2021).…”
Section: Basic Firefly Algorithmmentioning
confidence: 99%
“…Many research works were based on this algorithm to solve their problems. FireFly was used to solve the mono-processors hybrid flow shop problem (Dekhici & Belkadi, 2017), flexible operation scheduling (Fuyu, Weining, & Yan, 2018), facial expression recognition (Mistry, Zhang, Sexton, Zeng, & He, 2017), home care scheduling (Dekhici, Redjem, Belkadi, & Mhamedi, 2019), biomedical engineering (BME), healthcare (Nayak, Naik, Dinesh, Vakula, & Byomakesha, 2020), and also for image analysis (Dey, Chaki, Moraru, Fong, & Yang, 2020). Some researchers stated that FA was a powerful algorithm for solving even some of the NP-complete problems (Kumar & Kumar, 2021).…”
Section: Basic Firefly Algorithmmentioning
confidence: 99%
“…The brightness, position, and dynamic decision domain are updated and reiterated to find the next suitable firefly [59]. The iterative process of the algorithm is divided into brightness update, position update, and dynamic decision domain update [60].…”
Section: Firefly Algorithmmentioning
confidence: 99%