Conventional genomic selection approaches use breeding values to evaluate individual plants or animals and to make selection decisions. Multiple variants of breeding values and selection approaches have been proposed, but they suffer two major limitations. First, selection decisions are not responsive to changes in time and resource availability. Second, selection decisions are not coordinated with related decisions such as mating and resource allocation. We present three new genomic selection approaches that attempt to address these two limitations, which were designed by engineering students in a class project at Iowa State University. Compared with previous approaches using the same data set from the literature, two of these engineering approaches were found to be comparable to the state of the art, and the third one significantly dominated all the previous approaches.