پشتیبانی مبتنی بر آگاهی برای تجزیه و تحلیل شبیه سازی سلول های تولید

Simulation is a widely used approach for assisting design and improvement of manufacturing systems. It is a complex activity and needs a great deal of human expertise. Since the knowledge of analyzing simulation output for decision making is not inherently captured in the simulation modeling methodology, a framework that integrates simulation and knowledge-based decision analysis is needed. In this paper, we develop a knowledge-based system that cooperates with simulation for improving the performance of manufacturing cells. Using Axiomatic Design as a guideline, a hierarchical knowledge base structure that corresponds to the decision process is built. Our proposed knowledge-based system consists of a set of facts and three levels of rules in a hierarchy that is consistent with the manufacturing cell system configuration. The system demonstrates the effectiveness of utilizing Axiomatic Design concept when developing a knowledge-based system. The results of an industrial study show that our method contributes to improving the performance of manufacturing cells.

A manufacturing cell is a cluster of machines or processes in close proximity and dedicated to the manufacturing of certain identified part families that share similar manufacturing requirements. To improve design and performance of manufacturing cells, simulation has become an effective method for its versatility in modeling complex and dynamic operations. Nevertheless, improving the performance of a manufacturing cell is still a complex activity that not only is time consuming but also demands much human expertise in its decision making. In addition, the skills required to conduct simulation studies correctly and accurately are not widespread [13]. By using knowledge-based system techniques, these skills and knowledge for the simulation analysis process can be captured in a computer. This calls for the need of a framework that integrates simulation and knowledge-based decision analysis. According to the simulation outcome, the knowledge-based system will assist the decision process for the improvement of the manufacturing cell performance. However, since human experts typically do not express their knowledge in a well-structured manner during system development, knowledge-based systems often suffer from the problems of poor structure, redundancy, and difficulty in maintenance [6], [8] and [16]. To develop such a decision support system, a well-organized knowledge base structure that reflects how the human experts solve problems is essential. To meet this critical need, our research aims at the following objectives: 1. To develop a knowledge-based system that cooperates with simulation to support decision making for manufacturing cell performance improvement. 2. To construct a knowledge base structure in assisting the systematic development of our proposed knowledge-based decision support system. 3. To demonstrate the effectiveness of the knowledge base for decision support of manufacturing cell performance improvement. The research focuses on flow-line type manufacturing cells where parts travel from upstream to downstream workstations sequentially in a fixed route. Every workstation consists of machines, loaders (i.e. operators or robots), and a conveyor. The proposed knowledge-based system analyzes outputs from a simulation model of a manufacturing cell, determines whether the specified objectives are achieved, and identifies opportunities for improvement.

A knowledge-based system that cooperates with simulation has been developed. They compensate each other in assisting the decision making for manufacturing cell improvement. With our knowledge base decision support, the key bottleneck workstations as well as bottleneck resources are clearly identified. Hence, the improvement processes can be carried out precisely. In solving this decision process, a hierarchical structure of knowledge-based system is constructed. The Independence Axiom in Axiomatic Design has been followed during the establishment of the knowledge base structure. Unlike existing research that only attempt to simplify the structure of a knowledge base after it is built, our work emphasizes the development of a good knowledge base structure even before it is built. Such a sound structure will help build the knowledge base systematically with good solution efficiency and consistency. To demonstrate the effectiveness of our proposed knowledge-based system, a real industry case is used. The simulation results show that the suggestions provided contribute to increasing the throughput. In conclusion, the engineer can improve the manufacturing cell with the help of the knowledge-based system and the simulation. It reduces the burden of engineers by revealing the problem sources and providing recommendations for solving the problems. Moreover, the study demonstrates the applicability and usefulness of AD in the design of knowledge-based decision support system.