Abstract
Most decisions are made under some uncertainty about hte future. Most models do not take that explicitly into account. Is that a minor issue or a major problem? Is it wise to postpone the treatment of uncertainty to real-time operations, or is it crucial to handle uncertainty already when planning operations? The purpose of this lecture is to illustrate how deterministic models, even combined with sensitivity analysis, what-if-sessions or other similar tools, can lead to rather bad decisions. We focus on a case from less-than-truckload trucking, and show model in systematic and recognizable ways. We also show how to use these systematic differences to find good solutions to stochastic optimization problems without actually solving such problems.
