A hybrid optimization algorithm for the job-shop scheduling problem

2009 ◽  
Author(s):  
Qiang Zhou ◽  
Xunxue Cui ◽  
Zhengshan Wang ◽  
Bin Yang
Keyword(s):  
Optimization Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Optimization ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Optimization Algorithm

A New Hybrid Optimization Algorithm and its Application in Job Shop Scheduling

2011 ◽  
Vol 55-57 ◽  
pp. 1789-1793
Author(s):  
Xian Zhou Cao ◽  
Zhen He Yang
Keyword(s):  
Genetic Algorithm ◽  
Production Cost ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Hybrid Genetic Algorithm ◽  
Hybrid Optimization ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling ◽  
Hybrid Optimization Algorithm ◽  
Active Schedule

In this paper, a dual-resource constrained job shop scheduling problem was studied by designing a hybrid genetic algorithm based on Genetic Algorithm (GA) and Simulated Annealing (SA). GA is used to search for a group of better solutions to the problem of minimizing production cost and then SA is applied to searching them for the best one. The combination of GA and SA utilizes the advantages of the two algorithms and overcomes their disadvantages. The operation-based encoding and an active schedule decoding method were employed. This hybrid genetic algorithm reasonably assigns the resources of machines and workers to jobs and achieves optimum on some performance. The results of numerical simulations, which are compared with those of other well-known algorithms, show better performance of the proposed algorithm.

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