Answers to: What is more useful: robust optimization, or stochastic programming?http://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming<p>When looking at optimization under uncertainty, do you more often use robust optimization (parameters are known within some bounds), or stochastic programming (parameters follow a known distribution)?</p>enTue, 08 Mar 2016 10:02:20 -0500Answer by ocramzhttp://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/13426<p>When modeling uncertainty as bounds on a random variable you're implicitly assuming that everything outside those bounds will have probability 0. It's a pretty strong statement to make in some cases, especially because the consequences of unforeseen deviations (the infamous "black swans") can be catastrophic.</p>
<p>On a more technical note, the results of robust optimization apply for some very well defined cases, e.g. log-concave distributions over convex sets and specific (e.g. LP) structure of the optimization problem.</p>ocramzTue, 08 Mar 2016 10:02:20 -0500http://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/13426Answer by Alan Ererahttp://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/5747<p>I agree that the answer is problem dependent. In my own experience, I have found that both stochastic and robust models have been roughly equally useful in logistics applications.</p>
<p>I also think that it is typically much more difficult to build the right <strong>model</strong> that incorporates uncertainty, compared to cases where one chooses to build a deterministic model.</p>
<p>Here are some ideas I try to keep in mind when modeling that may help lead to an appropriate model:</p>
<ol>
<li>Nearly every model is an <em>approximation</em> of a real-world decision problem.</li>
<li>Many parameters used in optimization models are uncertain, but deterministic models where we assume a single value for each can still be the best choice.</li>
<li>If you think it is important to model the uncertainty in a parameter, remember that the user can choose <em>how</em> to model that uncertainty. Remember that fitting a probability distribution is yet another modeling approximation, and that choosing to model a parameter as known is the same as <em>modeling</em> its probability distribution as a unit point mass at its expected value.</li>
<li>Take time to consider when information that you assume to be unknown when planning becomes known, and how you will model the decision stages for your problem. For example, a multiple stage decision problem can still be <em>modeled</em> as a two-stage problem and lead to good decisions.</li>
<li>Strike the right balance between the detail in a model, and our ability to find optimal or nearly-optimal solutions to that model.<br>
</li>
</ol>Alan EreraThu, 21 Jun 2012 10:51:35 -0400http://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/5747Answer by Ehsanhttp://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/5730<p>The main issue to consider for making such decision is that which approach better suits your problem definition and available data. Hence, the answer is problem-dependent. However, some arguments could be made in favor of robust optimization from a (very) practical point of view.</p>
<blockquote>
<p>"In general, stochastic programming is
a more mature area within the
operations research field. However,
its application in practice is limited
by its heavy dependency on
availability of historical data and
complex modeling and computational
aspects for practitioners with limited
operations research knowledge. On the
other hand, robust optimization models
are usually more easy to understand
and implement. Also, they do not
significantly increase the complexity
of the considered optimization problem
in most cases." (See <a href="http://www.tandfonline.com/doi/abs/10.1080/00207543.2011.625051">here</a> for a brief discussion in the context of supply chain network design)</p>
</blockquote>
<p>Therefore, I believe robust optimization is more applicable in most common situations.</p>EhsanWed, 20 Jun 2012 06:02:00 -0400http://www.or-exchange.com/questions/5729/what-is-more-useful-robust-optimization-or-stochastic-programming/5730