The problem is solved up front, but must be resolved with constantly updating information.

By Static-Dynamic, we mean the optimizer presents a full solution, and then resolves and makes new solutions as the data changes.

A Static-Dynamic  model can provide very high degrees of solution quality, because the static part can be given many hours to come to an initial set of recommendations, whereas the dynamic resolve can ensure that real-time changes are reflected.

Static-Dynamic can provide a challenge in Human-in-the-Loop situations, as users typically do not want the optimization model to completely shuffle or change its recommendations for trivial reasons, especially when the user is spending many hours working through the optimization’s recommendations and validating them.

The complexity is that these same users do want re-solving (otherwise it would simply be a static solution, which is relatively easy) but only in cases where “it’s worth it.”   In many cases, state-of-the-art technical approaches are required to fulfill this seemingly simple and straightforward request to the satisfaction of the users.

  • For the airline, a full day’s (static) schedule is created by the optimizer in the early hours of each morning, and as the day develops, the optimizer resolves and changes its recommendations as new information is known (dynamic).  Because the planner/flight controller is speaking with the pilots in real time, the dynamic nature of this optimization is highly important.
  • For the high frequency hedge fund, the optimizer creates a desired portfolio (static) but then adjusts its buy/sell requests as potentially small changes occur in supply and demand (dynamic).
  • For the regional agribusiness, daily (static) plans are re-planned during the day as crops are harvested, loaded, and milled (dynamic) – squeezing out the maximum efficiency from people and equipment assets.