In a previous post, I discussed using discrete-event simulation to validate an optimization model and its underlying assumptions. A similar approach can be used to validate queueing models as well. And when it is found that the assumptions required for a queueing model are not a good fit for the
SAS/OR 14.1, which became available on July 14, delivers a number of new and enhanced features in optimization and simulation. These changes are designed to make SAS/OR even easier to use and to enable you to model and solve larger, more complex problems more efficiently. If you're using SAS/OR now,
During the week of July 13-17, 2015 most optimization experts will attend the 22nd International Symposium on Mathematical Programming (ISMP2015), which is this year's most important optimization conference. Several members of the SAS/OR team will attend. We will give various talks during the week, here is our schedule.
In 2013, Rick Wicklin blogged about visualizing matrices as heat maps using SAS/IML. That post reminded me that we had done a similar thing for the coefficient matrices in our optimization problems. In particular, we have developed some SAS macros to visualize the input data sets for the OPTLP (linear
Good Old Country-Style Optimization In an odd way, Imre Polik's recent post, How to solve puzzles? Peg solitaire with optimization, reminded me of one more reason why I like to eat at Cracker Barrel, an American chain of country-style restaurants.
The primary objective of many discrete-event simulation projects is system investigation. Output data from the simulation model are used to better understand the operation of the system (whether that system is real or theoretical), as well as to conduct various "what-if"-type analyses. However, I recently worked on another model