Optimal design generation: an approach based on discovery probability

Abstract

Efficient algorithms for searching for optimal saturated designs for sampling experiments are widely available. They maximize a given efficiency measure (such as D-optimality) and provide an optimum design. Nevertheless, they do not guarantee a global optimal design. Indeed, they start from an initial random design and find a local optimal design. If the initial design is changed the optimum found will, in general, be different. A natural question arises. Should we stop at the design found or should we run the algorithm again in search of a better design? This paper uses very recent methods and software for discovery probability to support the decision to continue or stop the sampling. A software tool written in SAS has been developed.

An Intelligent Decision Algorithm for the Generation of Maritime Search and Rescue Emergency Response Plans

“…In Fig. 9, three detection models are introduced for the relationship between POD and coverage representing the definite range model (see formula 5), the random search model (see formula 6) and the inverse cube model (see formula 7) [28], [29]. The calculation formula is as follows:…”