Congratulations Mubeen Ur Rehman, Licentiate in Mechanical Engineering!
- Post by: Tobias Larsson
- 18th September 2025
- No Comment
Mubeen Ur Rehman presented his licentiate thesis entitled “Model-Driven Design Decision Support Systems for Complex Engineering Systems: Challenges in Early-Design Stages” in front of a local crowd and online listeners.
Mubeen presented his research comprising of an introductory part and 4 research papers. His work focuses on model-driven decision support systems (DSS) in the context of complex engineering systems (CES). It systematically identifies and explores the challenges faced in integrating and utilizing DSS during the early stages of design. Through a literature review and empirical case studies within an industrial setting, the research highlights barriers such as the difficulty in integrating heterogeneous simulation models, the uneven maturity of DSS across domains, and the lack of intuitive interfaces and real-time feedback mechanisms. Ultimately, the thesis contributes to a deeper understanding of practical challenges to support both researchers and practitioners in developing more user-centered and fit-for-purpose DSS in complex design environments. Mubeen has conducted his research within applied projects at Blekinge Institute of Technology (BTH) funded via the Swedish Innovation Agency (VINNOVA) through the FELD project, part of the Fossil-free mobile work machine initiative, and the PROSUCCO – PRO SUstainable Co-COnfiguration initiative.
The opponent for the day was Senior Associate Professor Mehdi Tarkian from Linköping University. With his strong background in mechanical engineering and design automation applications, Mehdi served as an excellent opponent, critically examining the applicability of the proposed research from Mubeen. The discussion developed into a valuable reflection session for both Mubeen and the present audience.
At the end also the audience had the chance to ask questions on the research.
Professor Alessandro Bertoni then concluded by declaring that Mubeen had passed the exam and achieved the licentiate degree. The event was rounded up with classic swedish fika!
Thesis abstract
Early-stage design decisions in complex engineering systems play a critical role in defining the system’s lifecycle performance, cost, and viability. As engineering systems are becoming increasingly interconnected, cyber-physical and multi-disciplinary, traditional decision-making approaches based on historical data and expert judgments often fall short. To navigate the early-stage design decision challenges and support better decision-making, model-driven decision support systems have emerged as promising tools that allow for integration of simulations, optimization, and data-driven models. Yet, in the context of complex engineering systems, the effective implementation of decision support systems remains limited due to socio-technical challenges.
This thesis systematically investigates and identifies challenges through a literature review and empirical case studies in an industrial setting. The research identified interrelated barriers that hinder effective decision support systems integration and utilization. First, difficulty in integrating heterogeneous simulation models across varying levels of granularity remains technically and methodologically challenging. Second, the uneven maturity level of decision support systems across engineering domains limits their consistent use in collaborative environments. Third, a lack of accessible and intuitive interfaces negatively impacts the usability for non-expert stakeholders. Fourth, the absence of a real-time feedback mechanism limits their function as a boundary object. Fifth, the lack of metrics to gauge and communicate model maturity and reliability creates risks for misinformed decisions. Lastly, the lack of lifecycle management, including evolution, traceability, and reliability of the underlying models, is rarely supported, which undermines long-term sustainability and trust in the decision support systems.
Keywords: Decision-support-systems, model-driven, decision-making, systems engineering
Thesis link: https://urn.kb.se/resolve?urn=urn:nbn:se:bth-28436