توسعه ابزار پشتیبانی تصمیم گیری هوشمند برای کمک به طراحی سیستم های تولید انعطاف پذیر

The design of flexible manufacturing systems (FMSs) is an essential but costly process. Although FMS design appears to be an excellent area for applying artificial intelligence (AI) and computer simulation techniques, to date there have been limited investigations on integrating AI with the modular simulation software available for FMS design. In this paper an integrated approach for the automatic design of FMS is reported, which uses simulation and multi-criteria decision-making techniques. The design process consists of the construction and testing of alternative designs using simulation methods. The selection of the most suitable design (based on the multi-criteria decision-making technique, the analytic hierarchy process (AHP)) is employed to analyze the output from the FMS simulation models. Intelligent tools (such as expert systems, fuzzy systems and neural networks), are developed for supporting the FMS design process. Active X technique is used for the actual integration of the FMS automatic design process and the intelligent decision support process.

Flexible manufacting system (FMS) design is a very complex task due to two important characteristics: (a) The wide variety of alternative system control strategies and configurations available to the designer [1]; (b) FMS design is a task in which a variety of selection criteria are involved, many of which are difficult to quantify. Additionally, some criteria have to be balanced against each other while taking into account the preferences of managers of the firm [2] and [3]. Modeling techniques have been devised to model and evaluate FMSs prior to their installation. Modeling is advantageous since it is costly to evaluate the performance of an FMS after installation. Today, physical models, analytical models, discrete simulation models, and, more recently, knowledge-based simulation systems, have been used for this purpose. However, a major problem exists as current modeling techniques are unable to capture all the FMS design dimensions, i.e. they are not able to solve the FMS design problem as a whole. This is a consequence of local, myopic, and isolated approaches to FMS design [4]. Therefore, a new approach combining operational research and intelligent decision-making process is needed and a user-friendly interface can be considered as being an essential requirement. The approach introduced in this paper integrates initial FMS design, systems analysis, decision-making support and artificial intelligence (AI) techniques and methodologies into one system. Fig. 1 shows the outline of this integrative approach for FMS design. As Fig. 1 indicates, FMS design models are built based on the objectives obtained from engineers. The multi-criteria decision support technique, the analytic hierarchy process (AHP), is then used to choose the best design. AI techniques (expert system, fuzzy sets and neural network) are used for the FMS design initialization, analysis, and evaluation. In other words, the ongoing research project by the present authors tries to integrate the FMS simulation models, AI tools and the decision support system into a unified system. Thus, developing an integrative intelligent decision support system for the design of FMS is the core activity of this research. The expert system tool (AI-1, Fig. 1) is developed to (i) analyze output from an FMS simulation model, (ii) determine whether specified design objectives are met, (iii) identify design deficiencies or opportunities for improvement and (iv) propose designs which overcome identified deficiencies or which exploit improvement opportunities. In order to establish the FMS models and AI-1, three different sources of expertise have been consulted. One source is an industrial engineering group in a Hong Kong manufacturing company. Another source of expertise is from one of the research project directors who has had over 20 years of experience in the use of simulation techniques in process design. The third source is from the literature. In this research, the AHP technique has been employed to develop the decision-making support tool for FMS design. AHP applications in the FMS area have been proved to be effective by our colleagues [5]. However, applications of AHP still need human judgement and this relies on experienced technical operators. Fuzzy sets and neural network intelligent techniques are also implemented for assisting the development work. Fig. 1 shows the fuzzy sets tool (AI-2) and the neural network tool (AI-3) which have been built to support the evaluating of systems performance measures. Integration of FMS design system is also a very important task. In this research, there are tools for FMS design, simulation and decision-making support. All these tools are integrated in a unique environment. A user-friendly interface is needed for the whole system. The general programming languages such as Visual Basic and C/C++ are the preferred media among these tools. Active X technique is employed for integration. The Active X technique is developed by Microsoft Company for application integration. This technique allows Windows applications to control each other and themselves via a programming interface. This paper first reviews the current literature concerning FMS design. Secondly, the simulation of generic FMS models and the expert systems tool for initial FMS model building are presented. Thirdly, the AHP process and the intelligent tools (fuzzy sets and neural networks) for decision support are described. Finally, the integration of the intelligent decision support tools and the design procedure using Active X technique is discussed.

The research work described in this paper demonstrates that development of intelligent decision support tools for the design of FMS is now within the realms of possibility. The design of FMS is employed with a simulation approach supported by an expert system tool. Though more than one design is probable, a multi-criteria decision support method, AHP, is able to suggest the most suitable design, which is then supported by the tools of fuzzy sets and neural networks. The integration of simulation and multi-criteria decision support methods is tested in this research, and work to date indicates that it is likely to be a usable and promising methodology in FMS design. A certain degree of automation in FMS design is realized in this research by using the Active X technology. It is important that future research effort should be directed toward fully automating the interface between the simulation models and the intelligent tools, thus enhancing the intelligence of the design system and enlarging its knowledge base. To summarise, the current research has demonstrated that the use of an integrated methodology of system modeling and AI tools for FMS design has enabled system designers to improve the efficiency of the design task. The present research work will continue to test the system in practical situations. Enlarging the knowledge base is the main task so that the system would have the capability to design a suitable FMS system for various industrial sectors.