A Causal Decoupling Approach to Efficient Planning for Logistics Problems with Stateful Stochastic Demand

Future conceptions of agile, just-in-time fabrica- tion, 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.