Optimization for Stochastic Dynamic Decision Processes

In modern supply chain and logistics operations, planning is often done under incomplete information and plans are subject to updates once more information is revealed. For example, new demand may come in while operations are already in process, parts of the operations are unexpectedly delayed, or resource availability may change. All this requires dynamic updates of previous planning and changes the nature of decision making. Instead of finding the most efficient plans (already a quite complex endeavor), flexibility and anticipation of future changes become essential for effective operations. In this talk, we will introduce such stochastic dynamic decision processes and give an overview of the main solution strategies, amongst others, simulation approaches and reinforcement learning. With a few examples from ongoing research projects, we will illustrate that there is no “one size fits all”-approach, but that data and problem structure play an important role.