Application of Job-shop Scheduling Based on Hybrid Genetic Algorithm

This paper designs a hybrid genetic algorithm for job- shop scheduling combined with a Genetic Algorithm (GA), Simulated Annealing (SA), and a Tabu Search (TS) algorithm. It gives a method for initial population generation for avoiding unreasonable solutions, and introduces SA and TS into the genetic operation mechanism of GA for overcoming the lack of job-shop scheduling optimization. Simulated computations show the superiority of the hybrid genetic algorithm, and examples verify the algorithm's efficiency.