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OR 2018: International Conference on Operations Research in Brussels, Belgium

GAMS will be at the OR 2018 in Brussels, Belgium

We will be at the OR 2018, the International Conference on Operations Research in Brussels, Belgium.

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

 

You can also attend on one of our speeches:

Pre-Conference Workshop: GAMS - An Introduction

By Fred Fiand, GAMS Software GmbH
Tuesday, September 11, 3:00 pm - 5:00 pm

Get ready to learn the basics of GAMS to develop algebraic models and solve them using state-of-the art algorithms. In this workshop, the key concepts of GAMS and the fundamentals of the language (e.g. sets, data, variables, equations) will be introduced. The main part will consist of a demonstration, where we are going to build a simple optimization based decision support application from scratch. We show how GAMS supports an easy growth path to larger and more sophisticated models, promotes speed and reliability during the development phase of optimization models, and provides access to all of the most powerful solver packages. Along the way we will look at some of the data management tools included in the GAMS system and show how to analyze and debug large problems using the various tools available within GAMS. This introduction assumes no familiarity with GAMS. There will be time for questions both during and at the end of this workshop.

 

Model Deployment in GAMS

By Lutz Westermann, GAMS Software GmbH
Wednesday, September 12, 4:10 pm - 5:50 pm (Session WD-16)

In many cases, using GAMS in the typical fashion - i.e. defining and solving models and evaluating the results within the given interfaces – presents a sufficient way to deploy optimization models. The underlying field of mathematical optimization, in which the focus is not so much on visualization as on the problem structure itself, has remained a kind of niche market to this day. In the large and very extensive segment of business analytics, however, intuitive deployment and visualization are essential. Since these two areas are increasingly overlapping and in the context of the ever-increasing use of the Internet, interest in alternative deployment methods is also growing in the field of mathematical optimization. In this talk we will show how deployment options of GAMS models can look like. As an example, we present a web interface which is based on an R package called "Shiny". We will show how a model that was written entirely in GAMS can be deployed with this WebUI on either a local machine or a remote server (e.g. to leverage parallel computing) in just a few steps. While data manipulation, scenario management and graphical evaluation of the optimization results can then be performed from within the WebUI, the model itself is not changed. Therefore, the Operations Research analyst can keep focusing on the structure of the optimization problem while planners have a powerful tool to plan and visualize the results.

 

GAMS and High-Performance Computing

By Fred Fiand and Michael Bussieck, GAMS Software GmbH
Thursday, September 13, 9:00 am - 10:15 am (Session TA-5)

Solving challenging optimization problems often involves algorithms that could exploit the massive parallel computation power of modern High-Performance Computing (HPC) Architectures. GAMS has several features that support different modes of parallelism for some time. Recently, HPC capabilities of GAMS have been extended significantly. A link to parallel interior solver PIPS-IPM for large-scale block structured Linear Programs has been implemented. This link requires the user to communicate the model's block structure by variable annotation to the solver. GAMS facilitates user friendly annotation capabilities on the language level.  Experimental implementations of parallel model generation for certain LPs illustrate additional speedup potential. Furthermore, the novel GAMS Embedded Code Facility, which extends the connectivity of GAMS to Python, allows the implementation of HPC applicable decomposition methods. Via packages like mpi4py the Message Passing Interface (MPI) standard can be used to implement efficient communication between master and worker processes as for example occurring in Benders Decomposition Approaches.

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