News & Events

INFORMS International Conference 2018 in Taipei

GAMS will be at the 2018 INFORMS International Conference on Business Analytics & Operations Research in Taipei, Taiwan.

We will be at the 2018 INFORMS International Conference on Business Analytics & Operations Research in Taipei, Taiwan.

Visit our booth for a chat - give us feedback or share your experiences with GAMS. 

You can also attend on one of our talks:

Exam scheduling at United States Military Academy West Point

by Frederik Proske, Operations Research Analyst, GAMS Software GmbH
Tuesday, June 19, 11:00 am - 12:30 pm

Each term the United States Military Academy (USMA) needs to schedule its exams. About 4000 cadets taking 5 - 8 exams each, need to be placed in 11 time slots. Due to the short time frame, a feasible solution in the sense that no cadet takes more than one exam per time slot cannot be obtained with a single exam version per course. So called makeups, alternative exams in another period, solve this problem. Makeups are also used to improve additional objectives which occur at USMA like the number of consecutive exams per cadet. We consider two solution strategies: an IP approach as as well as a nonlinear approach based on LocalSolver. We report numerical experiments for both methods based on real world data from USMA.

Computing in the Cloud and High Performance Computing with GAMS

by Dr. Franz Nelißen, Managing Director, GAMS Software GmbH
Tuesday, June 19, 11:00 am - 12:30 pm

The General Algebraic Modeling System (GAMS) has evolved continuously in response to user requirements, changes in computing environments and advances in the theory and practice of mathematical optimization. The requirements of applications in areas like energy systems have increased to a level that is sometimes beyond the range of local computing resources. In this talk, we will look into some options to run GAMS models in different Cloud environments. We will also report on a project that uses High-Performance Computing resources to solve super-large energy system models using massive parallel computing.