Improved Genetic Algorithm for Solving Flexible Job Shop Scheduling Problem
Xiong Luo, Qian Qian, Yun Fa Fu
Abstract
The Genetic algorithm is one of the effective methods to solve flexible job shop scheduling problems. An improved genetic algorithm is proposed to overcome the shortcomings of traditional genetic algorithm, such as weak searching ability and long running time when solving FJSP. There are two main improvements. First, the algorithm adopted a new generation mechanism to produce the initial population, which could accelerate the convergence speed of the algorithm. Second, a new single-point mutation operation is designed to avoid the occurrence of illegal solutions, thus reducing the running time of the algorithm. The simulation results proved that the improved algorithm has better performance than some other algorithms.