A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling ProblemReport as inadecuate

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No-wait job-shop scheduling NWJSS problem is one of the classical scheduling problems that exist on many kinds of industry with no-wait constraint, such as metal working, plastic, chemical, and food industries. Several methods have been proposed to solve this problem, both exact i.e. integer programming and metaheuristic methods. Cross entropy CE, as a new metaheuristic, can be an alternative method to solve NWJSS problem. This method has been used in combinatorial optimization, as well as multi-external optimization and rare-event simulation. On these problems, CE implementation results an optimal value with less computational time in average. However, using original CE to solve large scale NWJSS requires high computational time. Considering this shortcoming, this paper proposed a hybrid of cross entropy with genetic algorithm GA, called CEGA, on m-machines NWJSS. The results are compared with other metaheuritics: Genetic Algorithm-Simulated Annealing GASA and hybrid tabu search. The results showed that CEGA providing better or at least equal makespans in comparison with the other two methods.


No-Wait Job Shop Scheduling, Cross Entropy, Genetic Algorithm, Combinatorial Optimization

Cite this paper

B. Santosa, M. Budiman and S. Wiratno -A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem,- Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 3, 2011, pp. 171-180. doi: 10.4236-jilsa.2011.33018.

Author: Budi Santosa, Muhammad Arif Budiman, Stefanus Eko Wiratno

Source: http://www.scirp.org/


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