Multimodal Optimization of Permutation Flow-Shop Scheduling Problems Using a Clustering-Genetic-Algorithm-Based Approach

Though many techniques were proposed for the optimization of Permutation Flow-Shop Scheduling Problem (PFSSP), current techniques only provide a single optimal schedule. Therefore, a new algorithm is proposed, by combining the k-means clustering algorithm and Genetic Algorithm (GA), for the multimod...

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Autori principali:	Pan Zou, Manik Rajora, Steven Y. Liang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2021-04-01
Serie:	Applied Sciences
Soggetti:	multimodal optimization Permutation Flow-Shop Scheduling Problem (PFSSP) k-means clustering algorithm Genetic Algorithm (GA)
Accesso online:	https://www.mdpi.com/2076-3417/11/8/3388
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