A hierarchical approach to planning production in complex manufacturing systems is presented. A single facilitg containing a number of work-centers that prvduce multiple part types is considered. The planning horizon includes a sequence of time periods, and the demand for all part types is assumed to be known. The production planning problem consists of minimizing the holding costs for the work...

For decision-theoretic planning problems with an indefinite horizon, plan execution terminates after a finite number of steps with probability one, but the number of steps until termination (i.e., the horizon) is uncertain and unbounded. In the traditional approach to modeling such problems, called a stochastic shortest-path problem, plan execution terminates when a particular state is reached,...

Following work on joint object-action representations, functional object-oriented networks (FOON) were introduced as a knowledge graph representation for robots. A FOON contains symbolic concepts useful to robot's understanding of tasks and its environment xmlns:xlink="http://www.w3.org/1999/xl...

We consider the class of linear programs that can be formulated with infinitely many variables and constraints but where each constraint has only finitely many variables. This class includes virtually all infinite horizon planning problems modeled as infinite stage linear programs. Examples include infinite horizon production planning under time-varying demands and equipment replacement under t...

A methodology is proposed to generate minimum-time optimal velocity profiles for a vehicle with prescribed acceleration limits along a specified path. The necessary optimality conditions are explicitly derived, allowing the construction of the optimal solution semi-analytically. A receding horizon implementation is also proposed for the on-line implementation of the velocity optimizer. Robustne...

Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step...