Genetic algorithm for the flexible job-shop scheduling problem
By Imed Kacem. SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483), 2003
380 citations recorded by the source corpus.
Subfields: Industrial and Manufacturing Engineering
View Paper: https://www.semanticscholar.org/paper/8474f43a520a2216ac8fdd8f1c9fe99c61ab9c20
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In Industrial and Manufacturing Engineering
Citations in the corpus
- A research survey: review of flexible job shop scheduling techniques (2016, background): “…Genetic Architecture (LEGA), that provides an effective integration between evolution and learning within a random search process Makespan 131 Pezzella et al. (2008) Research GA that integrates different strategies for generating the initial population, selecting the individuals for…”
- An effective genetic algorithm for the flexible job-shop scheduling problem (2011, methodology): “…Fuh, and Zhang (2003) presented a modified GA that is able to solve distribute scheduling problems and FJSP. Kacem, Hammadi, and Borne (2002) and Kacem (2003) used tasks sequencing list coding scheme that combines both routing and sequencing information to form a chromosome representation, and…”
- Sequence-dependent setup time flexible job shop scheduling problem to minimise total tardiness (2013, background): “Pezzella, Morganti, and Ciaschetti (2008) propose a genetic algorithm and Yazdani et al. (2009) present a parallel variable neighbourhood search for FJSSP.”
- A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems (2019, methodology): “Owing to such complexity, traditional mathematic optimization methods are difficult to tackle within a reasonable amount of time [5].”