Vendor Workshops

Saturday, November 12, 2016

 

Morning 9am – 11:30amAfternoon 12 – 2:30pmAfternoon 3 – 5:30pm
 SAS-GAP/EDU – Room 201A AIMMS – Room 201A SAS Institute – Room 201A
 Optimization Direct – Room 201B Bayesia – Room 202B GAMS – Room 201B
  FICO – Room 201B GUROBI – Room 202B
  SAS-JMP – Room 202A SIMIO – Room 202C
   MathWorks – Room 202A
   Neusrel – Room 208B
   LINDO – Room 208A
   Frontline Systems – Room 205B
   IBM – Room 205A
   AnyLogic North America – Room 205C

 

9:00am- 11:30am

Optimization Direct
Recent advancements in Linear and Mixed Programing give us the capability to solve larger Optimization Problems. CPLEX Optimization Studio solves large-scale optimization problems and enables better business decisions and resulting financial benefits in areas such as supply chain management, operations, healthcare, retail, transportation, logistics and asset management. In this workshop using CPLEX Optimization Studio we will discuss modeling practices, case studies and demonstrate good practices for solving Hard Optimization Problems. We will also discuss recent CPLEX performance improvements and recently added features.

SAS -GAP/EDU
SAS University Edition is a no-cost version of SAS software that runs on Windows, Mac OS X, Linux, or in the cloud using Amazon Web Services. SAS University Edition runs the latest SAS graphical user interface, SAS Studio, which is ideal for programmers and non-programmers alike: SAS University Edition is intuitive yet powerful and provides users with a supportive programming interface along with convenient point and click tasks and tools. In this demonstration, a variety of raw data from various sources such as a Microsoft Excel Workbook and a comma-separated values (CSV) file will be acquired and loaded, initial data quality assessments will be performed, data will be merged and analyzed, and final reports will be generated in .pdf and .rtf formats. Attendees will experience the conventional SAS Programmer and new Visual Programmer interfaces and will learn about free SAS learning resources. Whether new to SAS or looking to expand SAS skills, attendees will learn how SAS University Edition can support users at any skill level.

12:00pm -2:30pm

AIMMS
Tangible Ways to use our Prescriptive Analytics Technology to Quickly Solve Complex Business Problems and Bring Competitive Advantage to Your Business
Successful companies are using our technology to drive change and innovation in strategy and execution. Our clients have increased revenue and saved/avoided billions of dollars in costs. This workshop offers a practical and interactive approach for individuals who have been trying to solve tough problems and would like them solved much quicker. You may be trying to improve planning, reduce costs, optimize your supply chain or increase innovation. You might also be looking for a new way to update your organization, methods and tools. We will share:

  • How AIMMS can help you and your staff make better decisions
  • How to create cool custom Apps that provide your users with recommended actions
  • A proven methodology that outlines how to take your ideas and drive them to results
  • Real world examples and demos as inspiration

Presented By: Gertjan de Lange, Peter Nieuwsteeg, and Haraldur Haraldsson

Bayesian Networks & BayesiaLab
Artificial Intelligence in Marketing Science:  Marketing Mix Modeling and Optimization with Bayesian Networks & BayesiaLab

“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” Over the last century, various versions of this quote have been attributed to John Wanamaker, Henry Ford, and Henry Procter. Yet 100 years after these marketing pioneers, in this day and age of big data and advanced analytics, the quote still rings true. The current practice is often “more art than science.” The lack of a well-established marketing mix methodology has little to do with the domain itself. Rather, it reflects the fact that marketing is yet another domain that typically has to rely on non-experimental data for decision support. The most important thing we need to recognize about marketing mix modeling is that it is a causal problem. This means we are not looking for a prediction of an outcome based on the observation of marketing variables. Rather, we are attempting to manipulate the marketing variables to optimize the outcome. Thus, we must simulate interventions, not observations, and we must switch from observational inference to causal inference. This brings us to the Holy Grail of statistics, i.e. deriving causal inference from observational data. We introduce the fundamental concepts of graphical models and how they can help us perform causal identification, i.e. determine whether or not it is possible to estimate causal effects from observational data. For this, we require causal assumptions about the domain plus a decision criterion, such as the Adjustment Criterion. While this is straightforward in theory, the complexity of the marketing domain limits the practical application of this criterion. We introduce the Disjunctive Cause Criterion, which significantly reduces the number of assumptions required for causal identification and, thus, confounder selection. To proceed from causal identification to causal estimation, we now require an “inference engine.” In the simplest case, we could use a regression. However, with dozens of interacting variables, that is not practical. This is where Artificial Intelligence comes into play: we employ BayesiaLab’s machine-learning algorithms, which builds a high-dimensional Bayesian network model that represents the joint probability distribution of all variables. This causal inference engine plus BayesiaLab’s Target Optimization algorithm enable us to search efficiently for the ideal marketing mix.

FICO
How to Model, Solve, and Deploy Optimization Across Industries with FICO Xpress
At the core of FICO Xpress Optimization Suite are its solver libraries. In this session, hear about the latest enhancements to our linear, mixed-integer and nonlinear solvers. Besides an update on the latest performance improvements, you will learn about the new tuning capabilities as well as the recently added Python interface. We are launching a new development environment. Be one of the first to experience how quickly you can develop optimization models in Xpress-Mosel or even complete cloud based optimization solutions. To complete the workshop, we will show a number of case studies from various industries which combine predictive analytics with optimization models as well as show how to connect to and manage data from various sources. Presented By: Oliver Bastert, Jim Williams, Baykal Hafizoglu, and Michael Perregaard

 

SAS Institute Inc – JMP Division
Interactive Data Analysis and Modeling with JMP 13 Pro
JMP® Statistical Discovery Software is dynamic and interactive desktop software for data exploration, visualization, analysis, and modeling. In this workshop we’ll use case studies to illustrate how to interactively build, compare and deploy predictive models using the newly release JMP 13 Pro. We use the Graph Builder®, data filter and other tools to get to know and prepare our data. Then, we explore a variety of predictive models (decision trees, regression, neural networks, and penalized regression), and use the Prediction Profiler and the Solution Path to interactively explore parameters and select potential models. Finally, we compare a variety of competing models using Model Comparison, and use the Model Depot to easily deploy these models.  Presented By: Mia Stephens, JMP Academic Ambassador

3:00pm – 5:30pm

GAMS
Algebraic Modeling Languages (AML) are one of the success stories in Operations Research. GAMS is one of the prominent AMLs and during this workshop we will showcase fundamental principles, recent and ongoing developments, and our view of the future of AMLs. We will review some application examples done by our clients in academia and industry, in which optimization is an important element. These projects give insight into the complexity of tasks as well as the variety of environments in developing and deploying optimization applications.

Gurobi
Recent Developments in the Gurobi Optimizer
In this workshop we’ll talk about the latest developments in the Gurobi Optimizer. We’ll give an overview of our new Gurobi 7.0 release, which includes performance enhancements and several major new features. We’ll also talk about significant enhancements to the Gurobi Instant Cloud.

LINDO Systems
New Features to Speed Up Development and Solution of Your Planning Models
LINDO Systems is introducing a wide variety of new ease-of-use features as well as performance improvement in its optimization based planning tools. There are significant speedups for the integer solver and for the Global solver.  There are extensive new functionalities, such as for the connecting with arbitrary data sources, handling of multiple scenarios, debugging models, finding the K best solutions, matrix capabilities, etc. Through real world based examples, we will show you which tool is best for each situation.

MathWorks
MATLAB: A Platform for Data Analytics
MATLAB has evolved to become a platform for predictive and prescriptive analytics. Engineering, Finance, Data Science, and IT teams are using MATLAB to build today’s advanced analytics systems ranging from risk analysis to predictive maintenance and telematics to advanced driver assistance systems and sensor analytics. Join us to see how MATLAB can help you:

  • Access, explore, and analyze data stored in files, on the web, and from data warehouses
  • Clean, explore, visualize, and combine complex multivariate data sets
  • Prototype, test, and refine predictive models using machine learning methods
  • Build and solve prescriptive models and analyze results
  • Share your results with others

We’ll highlight our newest features for Big Data, machine learning, deep learning, and optimization through examples such as load forecasting, Monte Carlo simulation, predictive maintenance, and embedded sensor analytics.

NEUSREL/Success Drivers
NEUSREL is the leading software for exploring cause-effect networks using the Universal Structure Modeling approach. Because it leverages machine learning techniques it is a self-learning system and the only methodology to explore previously not hypnotized nonlinearities (of any form) or interactions (of any type). It is the first system meeting the need of researcher to explore success factors instead of just falsifying a giving set of hypothesis. It bridges the largely relevant gap between qualitative exploration and statistical testing/modeling. The WORKSHOP: We give a introduction into the approach of Universal Structure Modeling and will discuss prerequisites for causal claims. Further we will illustrate the practical relevance of the approach by sharing several case studies. Lastly we give a quick step by step life example about how to set up a model with the software, how to run it and how to interprete the results. All participants will get a trial version and can run their own data and direct questions to the presenter. The PRESENTER: Dr. Frank Buckler, the inventor of NEUSREL and the Universal Structure Modeling approach.  PREREADINGS: Paper – http://www.success-drivers.com/pdf/USM_MJRM_02-08-Buckler.pdf  Webcast recording – http://www.success-drivers.com/AMA-Webcast-March11-2016.mp4

SAS Institute
Solving Business Problems with SAS Analytics and OPTMODEL
SAS provides a diverse array of analytics, including data integration, statistical analysis, data/text mining, forecasting, and reporting, all integrated with operations research capabilities in optimization, discrete-event simulation, and scheduling. The most prominent SAS optimization component is the OPTMODEL procedure. OPTMODEL incorporates an algebraic optimization modeling language that provides unified support for LP, MILP, QP, NLP, CLP, and network-oriented models and methods, including an expanding suite of standard solvers and support for customized algorithms. We’ll demonstrate OPTMODEL’s power and versatility in building and solving optimization models, noting the significant improvements resulting from recently added features. We’ll emphasize integration with SAS data, analytic, and reporting capabilities, focusing primarily on case studies drawn from our successful work with customers across a broad range of industries.

SIMIO

The Coexistence of a Simulation Model and a Real-Time Planning & Scheduling Tool, on the Desktop and in the Cloud 
Come learn how a simulation model that can be used for facility design or business process optimization can also be used for real-time planning and scheduling.  We will discuss how one Simio tool is a dual purpose solution that can be used to address future planning decisions as well as daily, operational decisions.  Simio now leverages the cloud computing power of Microsoft Azure to support your most demanding applications. We will show how our Simio Portal Edition can be used for rapid experimentation or to simply share results across your enterprise.  Come explore an overview of the new Simio experience and see why we are always “Forward Thinking.”  Presented By: Renee Thiesing, Senior Applications Engineer, rthiesing@simio.com 412-259-5871

Frontline Systems, Inc
Full-Spectrum Analytics in Your Browser, Your Spreadsheet, or Your Own Application
Find your fastest path to real analytics results at this workshop, where business analysts, developers new to analytics, and experts are all welcome. We’ll show how you can build Big Data predictive analytic models with “just” a web browser and XLMiner®; solve the largest conventional and stochastic optimization models with “just” a spreadsheet and Analytic Solver® Platform; and create and run predictive or prescriptive analytics models in a single-page web or mobile app with “just” two mouse clicks and RASON®, our analytics API. Pull data into your analytic models, whether it’s in spreadsheets, databases, BI systems, cloud resources, or Apache Spark Big Data clusters. Easily move models between desktop and cloud, or between web browsers, spreadsheets, and production applications in a programming language. Learn analytic modeling as you work to get results, using our online courses, or in hundreds of university courses where our software is used. You’ll see why over 8,000 organizations have used Frontline Solvers over more than 25 years, and why over 200,000 users are already using our cloud analytics apps.

IBM

IBM Decision Optimization: Performance Gains and New Features 
In this tutorial, you will learn about:
*The new CPLEX and CP Optimizer engine features in the upcoming IBM CPLEX Optimization Studio release, including how to use the new features, how they can help you during the development of your model, and how they can speed up the resolution of your models.
*The new features in Decision Optimization on Cloud (DOcplexcloud), and the various ways to use it via the extensive set of APIs, as well as via IBM’s new Data Science Experience (DSX).

Follow us on twitter @IBMoptimization
Presented By: Xavier Nodet

AnyLogic North America

AnyLogic: A Journey Through the Latest Version
Take a journey through the newest version of AnyLogic with two of the masterminds behind AnyLogic as your guide. The workshop includes comprehensive how-tos for features of the newest version. We will update the workshop agenda with specifics, closer to the conference.

Presented By:  Andrei Borshchev, CEO of The AnyLogic Company and Nikolay Churkov, Head of Development at The AnyLogic Company