Discrete Cuckoo Search algorithm for job shop scheduling problem

Author(s):  
Aziz Ouaarab ◽  
Belaid Ahiod ◽  
Xin-She Yang ◽  
Mohammed Abbad
Author(s):  
Ahmed T. Saadeq Al-Obaidi ◽  
Samer Alaa Hussein

The Cuckoo Search (CS) is heuristic search algorithm which inspired from cuckoo bird behavior. In this paper, we proposed two improvements for the cuckoo search algorithm of solving Flexible Job-Shop Scheduling problem (FJSP); the first one depends on Best Neighbors Generation (CS-BNG) and the second one based on Iterative Levy Flight (CS-ILF). Some adaptation for the key points of CS algorithm has been done to enhance searching in the discrete state space. The proposed algorithms have increased solutions quality and convergence rate. The improved algorithms have been tested on some FJSP benchmark instances for performance examination. The experimental results demonstrate the effectiveness of the improved algorithms in comparison to the basic cuckoo search algorithm.


Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane

AbstractIn job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.


2019 ◽  
Vol 95 ◽  
pp. 04007
Author(s):  
Yan Ge ◽  
Aimin Wang ◽  
Zijin Zhao ◽  
Jieran Ye

To deal with the job-shop scheduling problem (JSP), a tabu-genetic hybrid search algorithm is proposed. The algorithm generates several initial solutions distributed in the whole solution space for tabu search by genetic algorithm, which avoids the over-dependence on the initial solution of tabu search algorithm. With the mechanism mentioned above, the algorithm proposed has both global search performance of genetic algorithm and local search performance of labu search algorithm. Finally, a program was developed with the achral data of FT (10x 10). to verify the feasibility and effectiveness of the algorithm. The result shows that the algorithm achieves satisfactory results in all indexes mentioned above.


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