Knowledge-Based System

Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou

Knowledge-Based System (KBS), a branch research area of AI, has been widely used in interpretation, prediction, diagnosis, debugging, design, planning, monitoring, repair, instruction, and control (Stefik et al., 1982) since it emerged in 1960s. KBS has been recognized as a promising paradigm for the next generation manufacturing systems and there is no doubt that the use of KBS in manufacturing will continue to expand, both in areas of application as well as in depth of knowledge. As a result, factories will benefit a lot, such as improved productivity, more stable and increased yields and increased asset utilization, all leading to improved factory performance. Now KBS are finding an increasing number of applications in almost each stage of intelligent manufacturing, including design, process planning and scheduling, production control, diagnosis and etc. Followed by a case study, the overview over all these applications will be discussed in this chapter after the key technologies of KBS are presented, including knowledge representation, knowledge use, knowledge acquisition and evaluation of KBS.

Tehnika ◽  
2020 ◽  
Vol 75 (6) ◽  
pp. 733-746
Author(s):  
Katarina Miljković ◽  
Milica Petrović

This paper gives a detailed state-of-the art in the research area o f the important function o f Intelligent Manufacturing Systems (IMS) - integrated process planning and scheduling o f manufacturing systems in dynamic environment (DIPPS). Referring to this, description o f the DIPPS problem is given, the criteria on the basis o f which the optimal rescheduling plan are formulated and considered, the adopted assumptions are defined and the mathematical model o f this problem is presented. Furthermore, the disturbances that occur in manufacturing systems are considered in detail: (i) machine breakdown, (ii) arrival of a new job and (iii) job cancellation. Approaches for solving DIPPS problems based on multiagent systems as well as approaches based on algorithms are analyzed. When it comes to approaches based on algorithms, the focus of this paper is on biologically inspired optimization algorithms: evolutionary algorithms, swarm intelligence based algorithms as well as hybrid approaches. The critical analysis within this research area is shown in order to conclude that biologically inspired artificial intelligence techniques have great potential in optimizing the considered IMS function.


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