An Improved Adaptive Genetic Algorithm for Job-Shop Scheduling Problem

2007 ◽  
Author(s):  
Yingjie Xing ◽  
Zhentong Chen ◽  
Jing Sun ◽  
Long Hu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

A Hybrid 2-Population Genetic Algorithm for the Job Shop Scheduling Problem

2014 ◽  
Vol 989-994 ◽  
pp. 2609-2612
Author(s):  
Zhuo Xu ◽  
Rui Wang ◽  
Zhong Min Wang
Keyword(s):  
Genetic Algorithm ◽  
Population Genetic ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Population Genetic Algorithm

In this paper, an analysis of a hybrid two-population genetic algorithm (H2PGA) for the job shop scheduling problem is presented. H2PGA is composed of two populations that constitute of similar fit chromosomes. These two branches implement genetic operation separately using different evolutionary strategy and exchange excellent chromosomes using migration strategy which is acquired by experiments. Improved adaptive genetic algorithm (IAGA) and simulated annealing genetic algorithm (SAGA) are applied in two branches respectively. By integrating the advantages of two techniques, this algorithm has comparatively solved the two major problems with genetic algorithm which are low convergence velocity and potentially to be plunged into local optimum. Experimental results show that the H2PGA outperforms the other three methods for it has higher convergence velocity and higher efficiency.

A New Adaptive Genetic Algorithm for Job-Shop Scheduling

2009 ◽  
Vol 626-627 ◽  
pp. 771-776
Author(s):  
Lei Wang ◽  
Dun Bing Tang ◽  
W.D. Yuan ◽  
M.J. Xu ◽  
M. Wan
Keyword(s):  
Genetic Algorithm ◽  
Production Efficiency ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Production Cycle ◽  
Adaptive Genetic Algorithm ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Slow Evolution

In order to minimize makespan for job-shop scheduling problem (JSP), an improved adaptive genetic algorithm (IAGA) based on hormone modulation mechanism is proposed. This algorithm has characteristics with avoiding overcoming premature phenomenon and slow evolution. The proposed IAGA algorithm is applied to dynamic job-shop scheduling problem (DJSP) and the satisfied result is obtained. By employing the proposed IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently. Therefore it embodies good adaptation to the DJSP (rush order, machine malfunction, and so on).

A Modified Adaptive Genetic Algorithm for the Flexible Job-Shop Scheduling Problem

2013 ◽  
Vol 401-403 ◽  
pp. 2037-2043
Author(s):  
Ying Pan ◽  
Dong Juan Xue ◽  
Tian Yi Gao ◽  
Li Bin Zhou ◽  
Xiao Yu Xie
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Flexible Job Shop Scheduling ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Combined with the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), a modified Adaptive Genetic Algorithm based on iterative generation and analysis of fitness values distribution is presented in this paper, which has both methods advantages. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using the modified AGA, which overcomes traditional GAs limitation that initial stage of evolution is early and random search of medium-late stage is slow. Such methods are verified to accelerate convergence process, enhance searching efficiency and solving precision as well as avoid low efficiency and local optimum.

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