Research on the flexible flow-shop scheduling based on the improved adaptive genetic algorithm

LIAO Shan，ZHAI Suo-xia，LU Yu-jun

（College of Mechanics and Automation，Zhejiang Sci-Tech University，Hangzhou 310018，China）

Abstract：Aiming at the flexible flow-shop scheduling problem，a modified adaptive genetic algorithm was given. A highly adaptive genetic mechanism and a definition method of highly adaptive fitness function were proposed. Corresponding adaptive selection，crossover and mutation operators were given. To solve the load balance of different machine in same working process，capacity coefficients which describe the processing capacity of the machine was presented to distributing the tasks to different machines on rotation. Finally，the simulation results validate the feasibility of the improved adaptive genetic algorithms.