TraDE: Training device selection via multi-objective optimization

Training planning is a recurring military problem. Since training programs can utilize multiple training devices with varying costs and training capabilities, selecting the types of devices required is a complex trade-off problem. Furthermore, the placement of these devices is critical due to the time and costs involved in travelling to and from the location of a training device. In this paper, we introduce a device bin-packing-and-location-based model, Training Device Estimation (TraDE), to study the computation of heterogeneous device mixes including the location of each device with respect to numerous objectives including various costs and training time. We apply the multi-objective Non-dominating Sorting Genetic Algorithm II to the TraDE model on a population represented by two-dimensional chromosomes. Finally, we also present a new mutation type to handle the nonlinearity inherent in a dual optimization problem which includes scheduling and location optimization. We clearly show that the new mutation operator produces superior results to the standard mutation operator.