Hybrid Genetic Algorithm for the Multi-objective Flexible Scheduling Problem

A hybrid genetic algorithm is proposed for multi-objective flexible job-shop scheduling problem, where the time, cost and equipment utilization rate are used as objective functions. First, the scheduling model for this problem is set up. Second, the matrix chromosome based on job-scheduling encode is adopted and it makes the decode and the use of belief operator much easier. Third, The objective functions are normalized in order to avoiding the preference to some objective with bigger quantity and AHP is used to transform the multi-objective problem into single objective problem. In order to accelerate convergence, a belief operator and neighborhood-search operator are integrated into the genetic algorithm. Finally, the simulations shows that the proposed method is efficient.