EFFECTIVE INTEGRATION OF DATA-DRIVEN SIMULATION AND OPTIMIZATION METHODS TO ENHANCE COMPUTER NUMERICAL CONTROL MANUFACTURING SYSTEMS

Abstract

Computer Numerical Control (CNC) machining plays a central role in industries such as aerospace, healthcare, defense, and oil and gas; sectors where precision, flexibility, and operational efficiency are paramount. On the other hand, expanding factories is constrained by the long lead-time required for procuring new CNC machines, partly due to global semiconductor shortages that have disrupted the availability of controller units and automation components. This thesis aims to explore existing research on these challenges and propose practical, data-driven strategies to support CNC factories in capacity and layout planning. To achieve this aim, the study follows a three-phase approach. The first phase involves a systematic literature review, conducted according to PRISMA guidelines, of relevant articles published in the Scopus database over the past two decades. In the second phase, a data-driven case study is conducted at a large CNC production facility. Empirical data collected from the plant informs the development of a discrete-event simulation model in AnyLogic, which is validated and used to conduct an As-Is analysis. The same model is then used in a simulation-based optimization framework that applies a Genetic Algorithm to identify improved capacity configurations under real-world constraints. In the third phase, the findings are further extended using the Systematic Layout Planning (SLP) methodology, and alternative layouts are evaluated using the Analytic Hierarchy Process (AHP). The literature review reveals a clear research gap in integrated approaches that connect CNC operational optimization with supply chain and spatial planning concerns. In the third phase, layout alternatives developed through SLP and scored via AHP showed the effectiveness of this approach in selecting candidate CNC plant layout when multi criteria are considered.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering