Multi-Objective Optimization for Dynamic Job-Shop Scheduling in Manufacturing Grid

This paper proposes a layered hybrid ant colony and genetic algorithm to solve the multi-objective optimization of dynamic job-shop scheduling problem in manufacturing grid. This algorithm is constructed in a layered structure, where the out layer uses ant colony algorithm to select the machine and the inner layer uses genetic algorithm with neighborhood search to optimize the job scheduling. We use a test example to show the feasibility and efficiency of the algorithm.