A hybrid genetic algorithm for the hybrid flow shop scheduling problem with nighttime work and simultaneous work constraints: A case study from the transformer industry

2015 ◽  
Vol 42 (15-16) ◽  
pp. 6196-6204 ◽  
Author(s):  
Sungbum Jun ◽  
Jinwoo Park
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Hybrid Genetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

scholarly journals Assigning dispatching rules using a genetic algorithm to solve a hybrid flow shop scheduling problem

2020 ◽  
Vol 42 ◽  
pp. 442-449 ◽  
Author(s):  
Benjamin Rolf ◽  
Tobias Reggelin ◽  
Abdulrahman Nahhas ◽  
Sebastian Lang ◽  
Marcel Müller
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Dispatching Rules ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Flow Shop Scheduling

Approach to Hybrid Flow-Shop Scheduling Problem Based on Self-Guided Genetic Algorithm

2015 ◽  
Vol 19 (3) ◽  
pp. 365-371 ◽  
Author(s):  
Wen-Zhan Dai ◽  
◽  
Kai Xia
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Probability Model ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Model Parameters ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Model Based ◽  
Hybrid Flow Shop Scheduling

The effective self-guided genetic algorithm (SGGA) which we proposed is based on the characteristics of a hybrid flow shop scheduling problem. A univariate probability model based on workpiece permutation is introduced together with a bivariate probability model based on a similar workpiece blocks. An approach to updating a probability model parameters is given based on superior individuals. A novel probability calculation function is proposed taking advantages of statistical learning information provided by univariate and bivariate probabilistic model to calculate the probability of workpieces located in different positions. A method for evaluating the quality of individual candidates generated by GA crossover and mutation operators is suggested for selecting promising and excellent individual candidates as offspring. Simulation results show that the SGGA has excellent performance and robustness.

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