Semiconductor manufacturing and supply chains
Chips are manufactured on silicon wafers through hundreds of complex process steps (front-end), dicing the chips, and assembling them into their final form (back-end). In the end, chips are bought by other businesses to make cars, computers, phones, etc. (end-production integration). My research spans the entire semiconductor supply chain and aims to develop production planning and control algorithms both at factory and at supply chain levels.
Selected research projects:
OUTPUT: Optimal Utilization Through Planning and prodUction intelligence (sponsored by Nexperia, 2017-2020).
SNAP: Smart Network for optimAl Planning (sponsored by Nexperia, 2019-2022).
Data-driven solutions to enable smart manufacturing for total quality management in the semiconductor industry (sponsored by NXP, 2023-2027).
Simulation modeling to support decision making in semiconductor supply chain planning (sponsored by NXP, 2023-2027).
AIMS5.0: Artificial Intelligence in Manufacturing leading to Sustainability and Industry 5.0 (sponsored by EU's Key Digital Technologies Joint Undertaking, 2023-2026).
Lifecycle logistics for capital goods
Timely and effective inspection and maintenance of capital equipment (e.g., photolithography machines in a chip factory, X-ray machines in a hospital, landing gears in an aircraft) is critical to efficiency and safety. Mismatches in the lifecycles of such equipment and their components can also result in obsolescence problems, resulting in high sustainment costs. My research builds optimization models (e.g., Markov decision processes, simulation-based solution techniques) to enable equipment suppliers and their users to increase equipment availability in an efficient way.
Selected research projects:
MANTIS: Cyber-Physical System based Proactive Collaborative Maintenance (sponsored by EU's ECSEL Joint Undertaking, 2015-2018).
Real-Time Data-Driven Maintenance Logistics (sponsored by NWO Dutch Research Council, 2017-2021).
MARCONI: MARitime Remote CONtrol Tower for service logistics Innovation (sponsored by NWO Dutch Research Council, 2018-2021).
Dynamic performance-based contracting in data-driven service logistics: Maritime industry specific Implementation (sponsored by NWO Dutch Research Council, 2021).
Digital twins
Companies can collect a lot of data from their operations and use computer simulations (digital twins) to evaluate and improve their operational decisions. However, it is not a trivial task to design the "best" simulation model that strikes a balance between predictive/prescriptive accuracy and what I call "maintainability" (the ability to adapt to changes in the system with minimal human intervention). By addressing this trade-off, my research aims to facilitate the adoption of digital twins by companies in managing their manufacturing and supply chain operations.
Selected research projects:
DARA: Data Analytics for trade lane Risk Assessments and control (sponsored by Dutch Institute for Advanced Logistics, 2017-2019).
LARA: Lane Analysis and Route Advisor (sponsored by Dutch Institute for Advanced Logistics, 2019-2020).
DayTiMe: Digital Lifecycle Twins for Predictive Maintenance (sponsored by ITEA - EU’s Eureka RD&I cluster on software innovation, 2018-2021).
DigiTWOP: Multi-level Digital Twin based Warehouse OPtimisation (sponsored by Dutch Institute for Advanced Logistics, 2024-2026).