Research on Optimized Problem-solving Solutions: Selection of the Production Process

In manufacturing industries, various problems may occur during the production process. The problems are complex and involve the relevant context of working environments. A problem-solving process is often initiated to create a solution and achieve a desired status. In this process, determining how to obtain a solution from the various candidate solutions is an important issue. In such uncertain working environments, context information can provide rich clues for problem-solving decision making. This work uses a selection approach to determine an optimized problem-solving process which will assist workers in choosing reasonable solutions. A context-based utility model explores the problem context information to obtain candidate solution actual utility values; a multi-criteria decision analysis uses the actual utility values to determine the optimal selection order for candidate solutions. The selection order is presented to the worker as an adaptive knowledge recommendation. The worker chooses a reasonable problem-solving solution based on the selection order. This paper uses a high-tech company’s knowledge base log as a source of analysis data. The experimental results show that the chosen approach to an optimized problem-solving solution selection is effective. The contribution of this research is a method which is easy to implement in a problem-solving decision support system.