Solving the Problem of General Job Shop Problem by Using Improved Cellular Genetic Algorithm
Zi Mu Li, Yi Zhang, Xiao Zheng, Xing Yu Wan · Advanced materials research · 2014
An improved cellular genetic algorithm (cGA) is proposed to study the optimization of the job-shop scheduling problem (JSP). Combining with the characteristics of JSP, a sequence-based coding mechanism is presented. The small overlapped neibhborhoods of cGA help to enhance the population diversity and exploration. An adaptive selection operation based on fitness of neighborhood is designed to prevent from getting into local optimal. The improved cellular genetic algorithm is tested on some instances and compared with simple genetic algorithm. The computational results show that the improved cellular genetic algorithm is effective on JSP.