Abstract:

A hybrid approach combining genetic algorithm (GA) and tabu search is designed to solve the joint lot sizing and scheduling problem, where GA is applied as a main frame to optimize lot sizing and the scheduling is optimized by tabu search alone, and the optimal solution of scheduling is returned to the main frame to generate integrated plans for continued searching. Different self-adaptive mechanisms are respectively used in selection operator and mutation operator to improve the search capability and convergence the speed of GA. Experiments are conducted on three kinds of different scaled problems. Compared with other algorithms, the obtained results validate the effectiveness of the proposed method.