http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Research on flexible job-shop scheduling problem based on a modified genetic algorithm
Wei Sun,Ying Pan,Xiaohong Lu,Qinyi Ma 대한기계학회 2010 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.24 No.10
Aiming at the existing problems with GA (genetic algorithm) for solving a flexible job-shop scheduling problem (FJSP), such as description model disunity, complicated coding and decoding methods, a FJSP solution method based on GA is proposed in this paper, and job-shop scheduling problem (JSP) with partial flexibility and JIT (just-in-time) request is transformed into a general FJSP. Moreover, a unified mathematical model is given. Through the improvement of coding rules, decoding algorithm, crossover and mutation operators,the modified GA’s convergence and search efficiency have been enhanced. The example analysis proves the proposed methods can make FJSP converge to the optimal solution steadily, exactly, and efficiently.
A new genetic algorithm for flexible job-shop scheduling problems
Imen Driss,Kinza Nadia Mouss,Assia Laggoun 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.3
Flexible job-shop scheduling problem (FJSP), which is proved to be NP-hard, is an extension of the classical job-shop schedulingproblem. In this paper, we propose a new genetic algorithm (NGA) to solve FJSP to minimize makespan. This new algorithm uses a newchromosome representation and adopts different strategies for crossover and mutation. The proposed algorithm is validated on a series ofbenchmark data sets and tested on data from a drug manufacturing company. Experimental results prove that the NGA is more efficientand competitive than some other existing algorithms.