Gonsalves Tad

In this paper we propose a composite-server model and make use of the knowledge of the intrinsic composition of its service providing units (personnel or equipment) to derive Qualitative knowledge-based rules for its performance evaluation. The composite server model that takes into account the composite nature of service has wider scope in its applications and can be used to represent a variety of system classes. We use this novel concept in the performance design and improvement of collaborative engineering systems. System modeling is done by Multi-Context Map (MCM) technique. MCM is a descriptive model that expresses the collaborative activity performed through the exchange of token, material and information; bottlenecks primarily arise due to the non-uniformity in the flow of token, material and information. Another source of bottlenecks in collaborative engineering systems is the lack or surplus of service-providing units, known as "Perspectives" in the MCM terminology. Bottlenecks due to inappropriate Perspective allocation are resolved by the Qualitative Reasoning approach. We have found this method successful in the performance design, evaluation and improvement of a practical collaborative engineering system presented at the end of this paper.

This paper discusses the design and implementation of a novel system performance improvement Expert System (ES) with a Qualitative inference engine. The motive for using Qualitative Reasoning is to overcome the computational complexity posed by the triple-input-triple-output contexts interactions in the Multi-Context Map (MCM) queuing network which models the system. The ES analyses the GPSS simulation data of system performance, consults the MCM knowledge base of the system, and with its inference engine driven by qualitative rules draws the parameter-tuning plan to resolve bottlenecks. The ES has been successfully applied in improving a typical benchmarking system in Collaboration Engineering.