Apply now for a PhD position at TUDelft on control for sustainable and reconfigurable manufacturing systems / value chains:
In today's rapidly evolving industrial landscape, the objective of any value chain is to sustainably utilize industrial systems to create outputs that maximize value for customers. However, achieving this goal requires navigating several challenges, both from an outward-looking perspective focused on customer value evolution and sustainability, as well as an inward-looking perspective concerning optimal resource utilization and intrinsic system disruptions.
Leveraging reconfigurable manufacturing systems (RMS) offers flexibility to adapt to changing demands and disruptions. Moreover, Production Planning and Control (PP&C) strategies play a relevant role in enhancing operational performance. These strategies span over different time horizons for effectively managing production activities, resource allocation, scheduling, and decision-making to optimize performance and achieve organizational goals. Feedback loops and real-time decision-making are particularly critical in complex and dynamic manufacturing environments where rapid adjustments are necessary to respond to changing conditions, such as fluctuations in demand, supply disruptions, or production constraints.
A research question arises: “How can planning and control strategies and mechanisms be designed and implemented to enhance the efficiency and reconfigurability of value chains in dynamic industrial environments?"
To explore this area effectively, the PhD candidate could delve into several interconnected research areas:
Integration of Model Predictive Control (MPC) in value Chain Planning and Control: Investigate how MPC principles can be adapted and applied to optimize planning and control processes across reconfigurable value chains. This involves developing MPC strategies that can dynamically adjust production plans, resource allocations, and scheduling decisions in response to changing demand patterns, disruptions, and sustainability requirements.
Reconfigurability in value chains: Explore the concept of reconfigurable value chains and how it can be leveraged to enhance flexibility, resilience, and responsiveness. Study different approaches to designing and implementing reconfigurable systems, such as modular production setups, flexible supply chain networks, and adaptive logistics solutions
Dynamic modeling and prediction: Develop dynamic models and predictive algorithms to capture the complex behavior of value chains over time. This involves analyzing historical data, identifying patterns and trends, and using predictive analytics to forecast future demand, supply, and market conditions. These predictive insights can then inform MPC strategies and decision-making processes within the value chain.
Optimization and decision support: Design optimization algorithms and decision support systems that integrate MPC and reconfigurability principles to maximize value creation and sustainability in the value chain. This includes formulating mathematical optimization models, implementing simulation-based approaches, and developing real-time decision support tools that consider multiple objectives, constraints, and uncertainties.
Case studies and applications: Apply the developed methodologies and tools to real-world case studies and industrial applications across different sectors, such as manufacturing, logistics, and distribution. Evaluate the effectiveness and practical feasibility of the proposed approaches in improving operational performance, reducing costs, and enhancing overall value chain resilience.
For more information regarding this position and the application procedure, see:
https://www.tudelft.nl/over-tu-delft/werken-bij-tu-delft/vacatures/details/?nPostingId=5087&nPostingTargetId=16640&
or contact Alessia Napoleone and/or Rudy Negenborn.