MODELLING AND DECISION SUPPORT SYSTEM FOR INTELLIGENT MANUFACTURING: AN EMPIRICAL STUDY FOR FEEDFORWARD-FEEDBACK LEARNING-BASED RUN-TO-RUN CONTROLLER FOR SEMICONDUCTOR DRY-ETCHING PROCESS

Abstract

This study aims to address the formation of various decision models based on modeling, analytics, and optimization techniques for intelligent manufacturing and smart production. Indeed, smart manufacturing deals with flexible decisions for addressing the dynamic, competitive, and global supply chains and production networks by employing advanced information technology, intelligent computerized control, digital decision technologies, and high levels of adaptability. While research in the broad area of smart manufacturing and its challenges in decision making encompasses a wide range of topics and methodologies, this study provides a good snapshot of current quantitative modeling approaches, issues, and trends. The validity of the proposed framework is estimated with a number of illustrations. This study concludes with discussion of future research directions.