Simon Lidberg
School of Engineering Science
Simon Lidberg defends his thesis "Decision Support Architecture: Improvement Management of Manufacturing Sites Through Multi-Level Simulation-Based Optimization".
Abstract
Manufacturing companies face a complex world with ever-changing demands, globalization, sustainability concerns, and economic challenges. Accurate improvement management and predicting future performance are crucial for staying competitive. Discrete-Event Simulation (DES) helps capture the dynamics of complex production systems, and Simulation-Based Optimization (SBO) can identify improvements with these models. However, past optimization approaches focused on individual production lines, which could lead to sub-optimizations when considering the performance of the manufacturing site. This research proposes a multi-level optimization approach for improvement management focusing on the performance of the manufacturing site.
High computational complexity resulting from combining several detailed models to create the site-level model is an obstacle to presenting decision support in a timeframe suitable for industrial decision-making. The research addresses this by validating a simplification method for DES models which replaces detailed models with simpler ones, sacrificing some detail and accuracy for faster runtime performance, enabling SBO for the site and supply chain level. This is the first major contribution of this dissertation.
The second part presents a Decision Support Architecture (DSA) using SBO to optimize site performance. The process starts by identifying the most critical bottleneck line on the entire site, including the specific parameter causing the issue, e.g., processing time or downtime. This approach prioritizes improvements with the highest impact for the least resource expenditure. Following this analysis, individual production lines are further optimized to identify specific equipment and parameters for improvement. Knowledge extraction algorithms then prioritize these improvements, guiding efforts and ensuring they benefit the entire site. Allowing for more efficient resource management, confidence that the proposed improvements are beneficial for the site, and improved decision-making at the site level.
The main novel research outcome of this dissertation lies in the multi-level optimization approach, combined with knowledge extraction and SBO enabled by simplified simulation models. This framework provides valuable insights for optimizing manufacturing sites in a complex and dynamic environment.
Supervisors
- Professor Amos Ng, School of Engineering Science, University of Skövde, Sweden and Division of Industrial Engineering and Management, Uppsala University, Uppsala, Sweden
- Tehseen Aslam, School of Engineering Science, University of Skövde, Sweden
- Marcus Frantzén, Evoma AB, Skövde, Sweden
Opponent
- Professor Sanjay Jain, School of Business, Department of Decision Sciences, George Washington University, USA
Committee
- Associate Professor Ann-Louise Andersen, Aalborg University, Department of materials and Production, Denmark
- Professor Patrik Jonsson, Chalmers University of Technology, Supply and Operations Management, Technology Management and Economics, Sweden
- Sandor Ujvari, School of Business, University of Skövde, Sweden
Doctoral thesis
2024
Contact
Industry-employed Doctoral Student
