Hybrid Genetic Algorithm for Flexible Job-shop Scheduling with Multi-objective

摘要

This paper presents a novel algorithm for multi-objective flexible Job-shop scheduling problem based on improved genetic algorithm. The new algorithm adopts non-binary encoding technique and handles the iteration using the dynamic crossover rate and mutation rate. At the same time, simulated annealing algorithm is applied during the stage of sorting the operations to solve the individual value of fitness. Experimental simulation shows that the algorithm is convergent, fast, and is a feasible and efficient method for solving the multi-objective flexible Job-shop scheduling problem.