International Conference on cybernetics and intelligent system, p.p , Sept Modeling Large-Scale Manpower Dynamics: An Expert Systems Approach

Outline Abstract Introduction Intelligent control approach to system modeling and design Case study : large-scale engineering manpower planning Conclusion References

Abstract Large-scale manpower planning systems are sociotechnical systems that are complex in structure and dynamic in behaviour. The method of system dynamics simulation has traditionally been applied to study such problems. However, lack of data and detailed information about the system structure can become obstacles in the modelling process. We propose the infusion of fuzzy expert systems with the system dynamics simulation method to overcome such challenges. In particular, the decision-making behvior and expectation formation structures are imitated with simple fuzzy expert systems. Computational experiments and results based on an actual case study of engineering manpower training shows very encouraging results of our proposed approach

Introduction The modelling, analysis and design of large-scale systems is often wrought with uncertainties, a major source of which is due to the lack of precise information and knowledge with regards to the system structure of interest. This is particularly prevalent in large-scale socio- technical systems involving human decision-making and behavioural aspects. System dynamics [Sterman, 2000, Sage and Rouse, 2009] is a system modelling tool used in the analysis and design of large-scale socio- technical systems. The tools of system dynamics consist of causal loop diagrams and stock-flow models to describe the various feedback loops linking the system variables.

Introduction The outline of the rest of this paper is as follows. In the next section, we propose a synthesis of fuzzy expert systems and the system dynamics simulation modelling method to support the modelling of imprecisely-known system structures. Of particular relevance for socio- technical systems are the modelling and identification of imprecise decision policies and information forecasting structures. We demonstrate through simple computational examples that fuzzy expert systems can accurately mimic the actual dynamic system behaviour even with imprecisely known structures. In Section II, a case study of a central manpower planning system with imprecise structures is presented. We report encouraging results from the application of our proposed intelligent control approach to the modelling of the system. Section III concludes our work

Intelligent control approach to system modeling and design Human decision making behavior is a very important element in any social-technical large scale systems problem that is often imprecisely described. Although the traditional system dynamics modeling paradigm emphasizes the importance of incorporating human decision making behavior, most proposed methods are typically very inaccurate and ad-hoc in nature.

Intelligent control approach to system modeling and design

Case study : large-scale engineering manpower planning

Conclusion In this paper, we advocate an intelligent control approach to design models of systems with imprecise structures. Our work is particularly relevant for socio-technical systems, since these often contain ambiguous relationships. Two such very prevalent imprecise relationships are concerned with the decision process of humans and their perception of state information.

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