Study On Knowledge-based Decision Making Mechanism For Dynamic Scheduling

As essential sector of production running in manufacturing system, the study on scheduling technology holds the balance in innovation and development of manufacturing enterprise. This dissertation focuses on the knowledge-based decision mechanism for dynamic scheduling. Its response strategy to uncertain incidents and knowledge-based decision model are studied, and with Contract Net Protocol as the coordinating platform, an implementation framework is built upon the multi-agent theory. As extensive research, the dissertation also involves the acquisition, fusion and update of scheduling knowledge.1. The entities, which are involved in scheduling system, are portrayed by all kinds of agents in the abstract. Integrating these agents, the structure of dynamic scheduling is constructed with Contract Net Protocol as the coordinating platform. This dissertation presents a dynamic scheduling decision mechanism based on the Fuzzy Petri Net theory. The Knowledge-based Decision Model, which belongs to the decision mechanism, takes standardized attributes knowledge and knowledge of scheduling rules to do fuzzy reasoning. As a result, the right resource is selected for each task to be performed. Upon that, dynamic scheduling is actualized. On the basis of capability evaluation, the Knowledge-based Decision Model integrates the degrees of attributes'credibility, the weight of each attribute and the degree of scheduling rule's credibility to make decision. In virtue of the decision-making which is carried out by qualitative analysis earlier and quantificational later, the dynamic scheduling leads to rational and effective.2. Through introducing the availability of resource and Prevailing Approximate Task, the approximation knowledge among tasks is defined. According to the availability of resources and the approximation of requirement among tasks, a new dynamic scheduling strategy is presented and a Multi-Stage Dynamic Scheduling Model is established. They avoid the needless interruption and abandonment of task, as a result, manufacturing system achieves decreasing the cost of adjusting tasks and the quantity of influenced tasks.3. Based upon the attributes of tasks and resources to accomplish these tasks: priority, cost, quality, load, time and so on, rough fuzzy methods, which extracts knowledge from the data set recording experts'action instances, are applied. In this way, the attributes defined in Rough Sets are generated from Membership Functions, and then, the grade knowledge is acquired in Fuzzy Cut-Set method. With the knowledge extracted from the data set recording experts'action instances, the decision table is built.