Automatic guided vehicle (AGV) fleet management always plays a significant role in smart manufacturing, which is widely studied as a representative nondeterministic polynomial-hard combinatorial optimization problem. With more smart factories featuring specialization in production line and human-robot interaction, AGVs are commonly bound with specific tracks, loading and unloading stations, which makes the current routing algorithms fail to play their path searching ability in complicated network topology. Thus, an integrated timetable optimization and AGV dispatching (TOAD) model is proposed aimed at such case, shifting the emphasis of routing to station selection and route selection from the perspective of timetable designing, while still considering the mixed directivity of layout, conflict avoidance, AGV availability and charging requirements. Targeted at makespan minimization, an improved genetic algorithm (GA) is used for solution with a heuristic operator to seek a better solution within shorter time. The proposed method is evaluated using an empirical factory case study with field data as input, with a comparison with the exact algorithm and standard GA. Results show that a smaller makespan and a shorter computation time can be obtained by the proposed TOAD model in large-scale scenarios, demonstrating a promising application prospect.