Future conceptions of agile, just-in-time fabrication, lean and “smart” manufacturing, and a host of allied processes that exploit advanced automation, depend in part on realizing improvements in logistics planning. The present paper hypothesizes that the key to improving flexibility will be the inclusion of sophisticated, time-correlated stochastic models of demand—whether that be demand by end-user consumers directly, or by other down-stream processes. Such dynamic models of demand, unfortunately, can greatly increase the space in which planning occurs when treated, as is common for planning under uncertainty, via the Markov Decision Processes formulation. To tackle this challenge, we identify three aspects that we postulate appear as commonalities in many logistics settings. They lead to an approach for approximate reduction of the planning problem via causal decoupling, which gives a spectrum of solutions where weakening time correlations affords faster optimization. Empirical results on small case studies —in lean manufacturing and commodity routing—show that retaining some limited (but non-zero) amount of temporal structure can provide a useful compromise between quality of the solution obtained and computation required.

We consider a robot tasked with observing its environment and later selectively summarizing what it saw as a vivid, structured narrative. The robot interacts with an uncertain environment, modelled as a stochastic process, and must decide what events to pay attention to (substance), and how to best make its recording (style) for later compilation of its summary. If carrying a video camera, for example, it must decide where to be, what to aim the camera at, and which stylistic selections, like the focus and level of zoom, are most suitable. This paper examines planning algorithms that help the robot predict events that (1)will likely occur; (2)would be useful in telling a tale; and (3)may be hewed to cohere stylistically. The third factor, a time-extended requirement, is entirely neglected in earlier, simpler work. With formulations based on underlying Markov Decision Processes, we compare two algorithms: a monolithic planner that jointly plans over events and style pairs and a decoupled approach that prescribes style conditioned on events. The decoupled approach is seen to be effective and much faster to compute, suggesting that computational expediency justifies the separation of substance from style. Finally, we also report on our hardware implementation.