Abstract

 
 
 We have developed a distributed DSS capable to working in a dynamic way. That is, when a domain of an organization needs a new kind of information, the system looks for this information. This system is based on the usage of mobile agents, which receive the user's queries and visit the appropriate DSS domains to gather the required information. The system itself must analyze where this information can be generated. To make this decision there is an intelligent agent (the Router) with a knowledge base (KB) where the information managed by each domain is represented. In this work, we present a strategy to obtain the initial data to be stored in the KB, a knowledge retrieval mechanism from the KB, and a learning mechanism so that the KB and the DSS operation can be continually improved. The proposed learning process is an interpretative case-based reasoning, which uses a set of rules to analyze the results of the information retrieval process and modifies the content of the router KB. Some examples are presented to illustrate the learning mechanism.
 
 
Highlights
Most organizations are horizontally structured, delegating decisions to their different sections
We briefly describe the information retrieval mechanism, which is explained in detail in [21] and we develop the learning mechanism, which allows updating the router knowledge base (KB) starting from the experience obtained with the system operation
The Router Agent (RA) is responsible for giving a route to the collecting agents, which must visit the system domains searching for an answer to queries formulated by a user
Summary
Most organizations are horizontally structured, delegating decisions to their different sections. In an industrial organization, enterprise domains such as Product Design, Planning, Control, Scheduling, Forecasting and Sales, are required to cooperate so as to achieve their common goal (see Figure 1). These domains constitute different decision points and are generally located on different functional units geographically disperse, use specific models and techniques to each decision type, and need to share knowledge and information. The enterprise domains change in time and they depend on human behavior, which sometimes may be unpredictable In these cases, the system must be able to search the ad-hoc required information analyzing where it is available or can be generated. The mobile agent technology appears as the most suitable one for this case ([18], [9])
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