Process Auxiliary Decision-making Based on Rough Sets and Regulation Distance Computing

Computer aided process planning（CAPP） is an important content of computer integrated manufacturing, and intelligentizing is the orientation of development of CAPP. Process planning has characters of empirical and time-consuming to finalize, and the same technical aim always can be achieved by different process schemes, so intelligentizing of process decision making always be a difficult point of CAPP and computer integrated manufacturing （CIM）. For the purpose of intelligent aided process decision making and reuse of process resource, this paper proposed a decision making method based on rough sets（RS） and regular distance computing. The main contents and methods of process planning decision making are analyzed under agile response manufacturing environment, the concept of process knowledge granule is represented, and the methods of process knowledge granule partitioning and granularity analysis are put forward. Based on the theory of RS and combined the method of process attributes importance identification, the paper brought forward a computing model for process scheme regulation distance under the same attribute conditions, and conflict resolution strategy was introduced to acquire process scheme fit for actual situation of enterprise＇s manufacturing resources, so as to realize process resources＇ conflict resolution and quick excavate and reuse of enterprises＇ existing process knowledge, to advance measures of process decision making and improve the rationality and capability of agile response of process planning.

KNOWLEDGE DISTANCE IN INFORMATION SYSTEMS

In this paper, we first introduce the concepts of knowledge closeness and knowledge distance for measuring the sameness and the difference among knowledge in an information system, respectively. The relationship between these two concepts is a strictly mutual complement relation. We then investigaie some important properties of knowledge distance and perform experimental analyses on two public data sets, which show the presented measure appears to be well suited to characterize the nature of knowledge in an information system. Finally, we establish the relationship between the knowledge distance and knowledge granulation, which shows that two variants of the knowledge distance can also be used to construct the knowledge granulation. These results will be helpful for studying uncertainty in information systems.