scholarly journals Investigating parallel genetic algorithms on job shop scheduling problems

1997 ◽  
pp. 383-393 ◽  
Author(s):  
Shyh-Chang Lin ◽  
Erik D. Goodman ◽  
William F. Punch
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Parallel Genetic Algorithms

Improved Adaptive Genetic Algorithms for Job Shop Scheduling Problems

2010 ◽  
Vol 97-101 ◽  
pp. 2473-2476 ◽  
Author(s):  
Mei Hong Liu ◽  
Xiong Feng Peng
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Mathematical Function ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
Adaptive Genetic Algorithms ◽  
Mutation Probability ◽  
Crossover Probability

In this paper, the adaptability of the genetic algorithm (GA) is considered. Two improved adaptive genetic algorithms (AGA) which are called Ch-AGA and Th-AGA for short are proposed based on the previous AGA. The crossover probability and the mutation probability of the Ch-AGA and the Th-AGA are non-linear changed between some a certain region, and adopted the mathematical function of chx and thx respectively. The two improved adaptive genetic algorithms are used to solve the classical job shop scheduling problems and the results indicate that the algorithms are more effective and more efficient than previous AGA, and should be used in practical applications.

scholarly journals Multipopulation Genetic Algorithms with Different Interaction Structures to Solve Flexible Job-Shop Scheduling Problems: A Network Science Perspective

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Ding-Shan Deng ◽  
Wei Long ◽  
Yan-Yan Li ◽  
Xiao-Qiu Shi
Keyword(s):  
Genetic Algorithms ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Structural Parameters ◽  
Small World ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Small World Networks ◽  
Shop Scheduling

Populations of multipopulation genetic algorithms (MPGAs) parallely evolve with some interaction mechanisms. Previous studies have shown that the interaction structures can impact on the performance of MPGAs to some extent. This paper introduces the concept of complex networks such as ring-shaped networks and small-world networks to study how interaction structures and their parameters influence the MPGAs, where subpopulations are regarded as nodes and their interaction or migration of elites between subpopulations as edges. After solving the flexible job-shop scheduling problem (FJSP) by MPGAs with different parameters of interaction structures, simulation results were measured by criteria, such as success rate and average optimal value. The analysis reveals that (1) the smaller the average path length (APL) of the network is, the higher the propagation rate will be; (2) the performance of MPGAs increased first and then decreased along with the decrease of APL, indicating that, for better performance, the networks should have a proper APL, which can be adjusted by changing the structural parameters of networks; and (3) because the edge number of small-world networks remains unchanged with different rewiring possibilities of edges, the change in performance indicates that the MPGA can be improved by a more proper interaction structure of subpopulations as other conditions remain unchanged.

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