自动制造系统的智能调度可有效控制系统的状态演化、优化生产周期和制造成本，并避免因规划不妥而造成的经济损失。状态空间爆炸是指系统可达状态数随系统规模呈指数增长，导致显示状态表示及其上的调度方法只能用于小型系统。课题围绕自动制造系统的状态空间爆炸问题，深入研究系统的隐式状态表示及其智能启发式调度策略，以实现具有一定规模、动态变化自动制造系统的符号化建模与智能调度控制。研究有序BDD技术应用于系统赋时一般Petri网的机制，构建时间和资源约束下模型状态集的布尔特征函数表示，建立基于操作相关度的变量顺序与BDD结构的优化匹配；研究基于状态属性的符号化启发函数，结合并行启发式A*搜索、局部变动下增量A*搜索、搜索导向和状态空间剪枝等关键技术，对变品种、动态连续生产自动制造系统的隐式状态表示和智能调度技术进行科学研讨，力求给出应用范围广、通用性较强、符合实际生产需要的系统状态建模与调度优化的技术实现。

The intelligent scheduling of automated manufacturing systems can effectively control the system state evolution and minimize the production cost and time. It also avoids poor production planning which often results in economic loss. On the other hand, state space explosion means the number of states in a system model often increases exponentially with the model size, which may cause that the explicit state presentation and the scheduling in such a state space can only be applied to small systems. To cope with the state space explosion problem in the automated manufacturing systems, this project will propose an implicit symbolic state presentation and its intelligent heuristic scheduling strategies, which is to provide an effective method to model and intelligent schedule reasonably sized and dynamically changing automated manufacturing systems. It will study how to apply the ordered binary decision diagraph (BDD) technique to the timed generalized Petri net models and define the Boolean characteristic functions of the state sets in the models with time and resource constraints. It will also research the optimal match of the variable order and the BDD structure based on relations between different operations. Furthermore, it will propose the symbolic representation mechanism of the heuristic functions based on state attributes and research the following key scheduling techniques: parallel heuristic A* search, incremental A* search with local variation, search guiding, state space pruning, etc. The project tries to investigate the strategies of implicit state representation and intelligent scheduling optimization of dynamic automated manufacturing systems with variant products. The goal of the project is to present a system scheduling optimization strategy and its technical implementation which can be applied widely and meet the needs of real production systems.