INDEX will study efficient incremental solutions to combinatorial optimisation problems occurring in design of computer experiments. Modern industrial processes often resort to simulation models of huge computational costs. Use of the original numerical codes for engineering tasks such as design optimisation and performance assessment, which require an intensive exploration of the model input space, would then require unrealistic amount of time. The current trend is to substitute the original numerical codes by a surrogate model of much lesser complexity, often a semi-parametric interpolator of a finite set of its outputs. The quality of the surrogate model depends on the set of simulation inputs (the design) used for this construction, and, obviously, it increases with design size. Classical approaches to design of experiments consider the design size N as a fixed parameter and try to optimise the information in the overall set of N points. However, in many situations the model simulations are progressively integrated, and a decision to stop the learning process is done on-line, based either on the estimated quality of the surrogate model already built or, more pragmatically, because the available (time, cost) budget has been totally consumed. In this context, it is important that the order of execution of the design points be well chosen, such that for all n