A HYBRID GENETIC ALGORITHM TO SOLVE MULTI-OBJECTIVE FUZZY FLEXIBLE JOB SHOP SCHEDULING PROBLEM
Journal: Frontiers in Manufacturing Engineering (FME)
Author: Kuan Liu, Hui Cheng, Xinyun Zhang
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
In this paper, a hybrid genetic algorithm is introduced to overcome the toughest combinatorial optimization problem, the fuzzy-flexible job-shop scheduling. In consideration of the multi-product-and- small-batch production characteristic in aerospace equipments manufacturers, a framework based on hybrid genetic algorithm aimed at minimizing the max makespan and tardiness of workpieces is built to solve the fuzzy flexible job shop scheduling problem. The logistic chaotic mapping model and heuristic rules are introduced in this hybrid genetic algorithm which separates the mutation operation from crossover operation in order to prevent the local optimum happening. This algorithm employs the single point crossover method which protecting the order of procedures to ensure the convergence precision. Meanwhile, experiments are designed to demonstrate the efficiency and feasibility of the Improved Chaotic Genetic Algorithm.