Time Dependent Hartreefock And Beyond

Moving assembly lines are the stepping stone for mass production of automobiles. Here, every second counts, which necessitates planners to meticulously optimize them. A crucial factor is each worker’s nonproductive walking time between the moving workpiece and line-side material containers for picking up required material. Minimizing the walking time is difficult because the workpiece moves steadily. Helmut A. Sedding devises algorithms to optimize the sequence of work operations, and the placement of material containers. Thereby, he introduces a novel category of time-dependent scheduling problems, and lays the basis for the algorithmic optimization of time-dependent paths at the moving assembly line.

by Joanne Hort (Editor), Sarah E. Kemp (Editor), Tracey Hollowood (Editor)

This book provides a novel framework for understanding and revising labor markets and education policies in an era of machine learning. It posits that while learning and knowing both require thinking, learning is fundamentally different than knowing because it results in cognitive processes that change over time. Learning, in contrast to knowing, requires time and agency. Therefore, “learning algorithms”—that enable machines to modify their actions based on real-world experiences—are a fundamentally new form of artificial intelligence that have potential to be even more disruptive to labor markets than prior introductions of digital technology.