Information Flow Based Decomposition of Decision-Making Problems Involving Partial Observability

Industrial planning and scheduling decisions are often inter-dependent. For example, planning level capacity allocation decisions affect production scheduling. While independent decision rules fail to address the above mentioned inter-dependence, simultaneous consideration of all interactions leads to a very large problem, which is oftentimes computationally intractable. In this study, we demonstrate, in the context of a machine maintenance problem for a reentrant flow system, a middle-ground approach by recognizing paths of strong information flow and then systematically decomposing the problem to be able to obtain a computationally tractable problem yielding a near-optimal decision policy. In the process, we make combined use of rigorous probability theories, approximate dynamic programming and simulation based rules.