Liu Yusong, student at the Institute of Radio Electronics and Information Technology – RTF, Ural Federal University named after the first President of Russia B.N. Yeltsin
Balungu Daniel Musafiri, PhD student, Assistant at the Basic Department of Big Data Analytics and Video Analysis Methods, Institute of Radio Electronics and Information Technology, Ural Federal University named after the first President of Russia B.N. Yeltsin
Abstract
In the era of smart manufacturing, optimizing workshop layouts plays a pivotal role in enhancing production efficiency and reducing logistics costs. This study proposes a digital twin-enhanced multi-objective layout optimization model that integrates Systematic Layout Planning (SLP) with a Genetic Algorithm (GA). The model aims to balance logistics efficiency and non-logistics relationships through a comprehensive optimization process. Using a PCB manufacturing workshop as a case study, the proposed SLP-GA model was verified via digital twin simulation built in Anylogic, which enabled dynamic evaluation of production flow and personnel utilization. Results demonstrate that the optimized layout reduces logistics distance by 28.8%, decreases transport personnel by 11, and increases annual output by over 120% compared with the original configuration. The proposed approach effectively supports lean transformation and provides a replicable framework for intelligent, data-driven workshop design in the context of Industry 4.0.
KEYWORDS: smart manufacturing, layout optimization, SLP-GA, digital twin.
Download article OPTIMIZING SMART MANUFACTURING WORKSHOP LAYOUTS USING A DIGITAL TWIN-ENHANCED SLP-GA MULTI-OBJECTIVE MODEL: A PCB CASE STUDY
