Discrete Event Dynamic Systems (abbreviated as DEDS) as an actively advancing aspect are paid more and more attention by people studying in the field of systems and controls. This paper centers around one of the applied aspect of DEDS, that is the scheduling and control of manufacturing system. The contents of this paper are done by the author during her graduate study and divided into four chapters. Chapter 1 is Introduction. First outline some important methods by which we study DEDS at present, and then explain them respectively. Finally introduce the way by which we do research in the field of the scheduling and control of manufacturing system. Chapter 2 is concerned about the real-time scheduling policies of manufacturing systems. The chapter outlines the Gershwin's hierarchical scheduling scheme in which various decisions and events are grouped into various level of a hierarchy according to their characteristic frequencies. The least frequent events are assigned to the top level which generates the decision parameters of the policy, such as the demand rate of each part type. The middle level computers the short-term production rates for each part type for each machine state. The lower level decides the time and the order to produce the parts, i.e. deciding the scheduling policy, with the aim of maintaining the real production rates equal to the production rates computerd by the middle level. The Perkins and Kumar have done some research in the lower level. The characteristics of their model is of feedback, stability and real-time. But the policies of CLB and CAF developed by them did not reflect the thought of optimization. So, based on their work, we develope a new capacity model and provide an optimizing aim for the lower level. Then we obtain a new scheduling policy according to optimizing principle. We also have proven the stability of this policy. In order to show the superiority of this policy, we give an example and compare the result of this policy with the results of CLB and CAF policies. The comparing results demonstrate the superiority of the new policy obtained by us. Chapter 3 talks about the modeling, the analysis of performance and the optimizing control of production line systems. Generally we model production systems by three methods: Queuing theory, Markov process theory and approximate method. The performance measures discussed by this chapter include the availability and the throughput of systems. We conside an assembly-like flow system where the assembly machine is the bottleneck of the system. For convenience, we decompose the system into two parts, one is assembly, the other is output. We model the assembly part by the approximate method and derive an approximate expression for the steady-state average throughput of the part according to the renewal reward theory. We also indicate how it can be used to buffer allocation which is a nonlinear program. In the o
修改评论