An improved multi-population genetic algorithm for job shop scheduling problem

2010 ◽  
Author(s):  
Ming Huang ◽  
Pengfei Liu ◽  
Xu Liang
Keyword(s):  
Genetic Algorithm ◽  
Population Genetic ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Population Genetic Algorithm

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.

Association Rule Mining for Job Shop Scheduling Problem Based on Genetic Algorithm

2012 ◽  
Vol 629 ◽  
pp. 730-734 ◽  
Author(s):  
Cun Liang Yan ◽  
Wei Feng Shi
Keyword(s):  
Genetic Algorithm ◽  
Association Rule ◽  
Association Rule Mining ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Rule Mining ◽  
Concept Hierarchy ◽  
Shop Scheduling

Job shop scheduling problem (JSP) is the most typical scheduling problem, In the process of JSP based on genetic algorithm (GA), large amounts of data will be produced. Mining them to find the useful information is necessary. In this paper dividing, hashing and array (DHA) association rule mining algorithm is used to find the frequent itemsets which contained in the process, and extract the corresponding association rules. Concept hierarchy is used to interpret the rules, and lots of useful rules appeared. It provides a new way for JSP study.

The Study of Job Shop Scheduling Based on Genetic Algorithm

2011 ◽  
Vol 201-203 ◽  
pp. 795-798
Author(s):  
Jun Xing Xiong ◽  
Jin Ping Zhao ◽  
Hai Ning Tu
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

Aiming at Job Shop Scheduling Problem with Minimal Makespan, This paper is designed to use genetic algorithm to solve the problem of job shop scheduling, and also achieves the algorithm by using the C#. Application shows that the genetic algorithm to solve job shop scheduling problem is efficient and has good application value.

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