An effective genetic algorithm for the flexible job-shop scheduling problem

By Guohui Zhang, Liang Gao, Yang Shi. Expert systems with applications, 2011

478 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/9730e6b42711dd2302e108108747de8038dbea6d

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In Industrial and Manufacturing Engineering

Citations in the corpus

Genetic algorithm-based heuristic for feature selection in credit risk assessment (2014, methodology): “To enhance the quality of the initial solution, Zhang et al. (2011) designed a new initial assignment method to generate a high-quality initial population that integrates different strategies to improve the convergence speed and the quality of the final solutions.”
An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem (2015, methodology): “Zhang et al. developed a genetic algorithm and proposed two assignment methods: Global Selection (GS) and Local Selection (LS) to generate high-quality initial population in the initialization stage and accelerate the convergence speed (Zhang et al. 2011).”
A review on swarm intelligence and evolutionary algorithms for solving flexible job shop scheduling problems (2019, methodology): “1) hGA with variable neighborhood descent [7] The hGA algorithm embeds two local search operators based on a critical path to GA.”
A research survey: review of flexible job shop scheduling techniques (2016, background): “…between workstations is limited Makespan 9 Al-Hinai and Elmekkawy (2011bb) Research Two-stage GA, FJSSP with random machine breakdowns Makespan 54 Zhang et al. (2011) Research GA with global selection, local selection, and random selection procedures for generating initial solution Makespan…”
An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem (2016, result): “…for generating the initial population, selecting the individuals for reproduction and reproducing new individuals to solve FJSP. Giovanni and Pezzella (2010) proposed an improved GA to solve the distributed FJSP. Zhang et al. (2011) proposed a modi ﬁ ed GA for FJSP and obtained good results.”