Capacity management for personalized services in education

Throughout the world, educational service systems take steps toward personalized educational models which recognize learner differences and give the flexibility to learners to demand for tailor-made services that match their actual learning needs and goals. However, as educational service systems take steps toward personalized learning, the logistics planning aspects of satisfying learners’ instructional needs become more complex, dynamic, and challenging due to the evolving and uncertain dynamics of individual learning demands. In contrast to one-size-fits-all educational systems in which learners are put into fixed groups based on their age and given the same type of instructional services, personalized learning systems need new types of logistics planning tools to manage their available service capacity.


To address this gap, this thesis develops several systematic optimization-based capacity management tools, in four research projects, for the use of service providers and learners in personalized learning systems, to enable the efficient logistics of instructional services. In providing these capacity management tools, we develop a variety of Operations Research models and methods. Among these are meta-heuristics, simulation, integer programming, chance-constrained programming, model predictive control, queueing theory and Markov decision processes. In numerically validating and demonstrating the effectiveness of the presented planning tools, we mainly rely on the data coming from Dutch secondary education schools, through collaborating with the Zo.Leer.Ik! schools network.