Combined with the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), a modified Adaptive Genetic Algorithm based on iterative generation and analysis of fitness values distribution is presented in this paper, which has both methods advantages. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using the modified AGA, which overcomes traditional GAs limitation that initial stage of evolution is early and random search of medium-late stage is slow. Such methods are verified to accelerate convergence process, enhance searching efficiency and solving precision as well as avoid low efficiency and local optimum.
Solving the Flexible Job Shop Scheduling Problem With Makespan Optimization by Using a Hybrid Taguchi-Genetic Algorithm