Exhibitor Workshops

Please note: Exhibitor Workshops are part of the preconference programming. As such, they may include product demonstrations and be commercial.

Sunday, April 12

11am-12:45pm

Model and Solve Nonlinear Optimization Problems with Artelys Knitro
Presented by: Louis-Pierre Campeau

Description

Artelys Knitro is the leading solver focused on large-scale, nonlinear (potentially non-convex), optimization problems. Knitro offers both interior-point and active-set algorithms for continuous models, as well as tools for handling problems with integer variables and other discrete structures. This tutorial will introduce the key features of Knitro and demonstrate how to use Knitro to model and solve optimization problems in various environments. The talk will showcase several instances of nonlinear problems, typically addressed through linear relaxation, yet warranting a direct approach. It will delve into the methodologies and tools employed to tackle these challenges. This tutorial aims at an audience familiar with the basics of mathematical optimization and Python programming but will mostly be focusing on practical examples.

11am-12:45pm

SAS Analytics
Presented by: Rob Pratt

Description

SAS offers extensive analytic capabilities, including machine learning, deep learning, natural language processing, statistical analysis, optimization, and simulation. SAS analytic functionality is also available through the open, cloud-enabled design of SAS® Viya®. You can program in SAS or in other languages – Python, Lua, Java, and R.

This workshop will review the SAS analytics portfolio, highlight recently added optimization features, and explore several optimization examples.

Academic faculty, staff, and students can access the latest SAS software through our free academic software platforms SAS Viya for Learners and SAS Viya Workbench for Learners. Access instructions and learning resources will be provided.

1-2:45pm

Scaling Adaptive Workforce Optimization: How ATALIAN and DecisionBrain Replaced Manual Scheduling with Real-Time Intelligence
Presented by: Tom Heyward

Description

Airport cleaning operations run against one of the most demanding scheduling environments in facility management — shifting flight schedules, unpredictable passenger flow, and cascading task dependencies that render static daily plans obsolete within hours. This workshop presents how ATALIAN, a global leader in facility management, partnered with DecisionBrain to replace manual coordination with a dynamic, closed-loop scheduling system powered by the DB Gene platform.

Attendees will see how real-time external data — including live flight information and zone-level crowd distribution — feeds directly into optimization logic to continuously revise workforce schedules as conditions evolve. We walk through a three-tier decision support model serving managers, supervisors, and frontline agents, and examine how role-specific workflows drove adoption across the organization.

Beyond the airport context, the session draws out the transferable principles: how situation-aware constraints, automated re-scheduling, and real-time execution tracking apply across any workforce-intensive environment facing volatile demand.

Participants leave with a practical framework for evaluating the transition from static, experience-dependent planning to standardized, AI-driven workforce optimization — and a clear picture of what production-ready decision intelligence looks like at enterprise scale.

1-2:45pm

Bridging Optimization and Machine Learning: An Exploration with GAMSPy
Presented by: Adam Christensen & Steven Dirkse

Description

This workshop introduces GAMSPy – a powerful mathematical optimization package that merges the high-performance GAMS execution system with the flexibility of the Python language. The result is a complete mathematical optimization package that can be deployed anywhere – effortlessly.

Join us to explore GAMSPy’s fundamental functionalities through practical exercises. We’ll cover everything from defining sets, parameters, variables, and equations to solving models and retrieving results, all within a familiar Python environment. Beyond the basics, we’ll also provide a glimpse into more advanced features, demonstrating how GAMSPy can streamline complex modeling workflows and enhance your analytical capabilities.

A major focus of the session is the intersection of optimization and Machine Learning (ML). We will explore essential GAMSPy ML functionality – such as formulations for neural network layers, activation functions, and regression trees. These tools enable users to implement sophisticated constructs directly within their optimization models, offering flexibility across diverse use cases.

Whether you’re a seasoned GAMS user looking to integrate with Python or a Python user curious about optimization, this workshop will equip you with essential skills needed to get started and demonstrate what is possible with GAMSPy.

1-2:45pm

Want Your Decision Project to Succeed? Treat it Like Software
Presented by: Nicole Misek and Ryan O’Neil

Description

Decision models drive significant ROI in cost savings, carbon emissions, customer satisfaction, personnel safety, and more. But there are overlooked inefficiencies outside the solver that compound during development, eroding stakeholder trust and confidence. And when it comes time to plug your model into software architectures, the project flounders and fails.

Much of OR training and education has traditionally focused on the elegant and powerful math driving decision models. More recently, leaders within the community are realizing that a software-conscious mindset is necessary for the discipline’s success. The good news: The tools and practices best suited to address this gap are more accessible than ever before.

This session will cover how to leverage the Nextmv platform to easily incorporate DecisionOps into OR and decision science projects. We’ll walk through hands-on exercises that demonstrate using an open source framework for structured local model development that can progress into robust model management, versioning, monitoring, testing, collaboration, and orchestration.

Throughout, you’ll see how to engage stakeholders efficiently by comparing individual model runs, performing scenario tests, comparing multiple models, and seamlessly integrating into the larger decision stack.

Join us for this interactive session that will demonstrate these workflows, allow for hands-on experimentation, and Q&A. This session is relevant to anyone working with tools such as: OR-Tools, Pyomo, HiGHS, Vroom, Gurobi, AMPL, Hexaly, FICO Xpress, NVIDIA cuOpt, IBM CPLEX, CVXPY, and more.

3-4:45pm

Unlocking Business Value with the Latest from FICO® Xpress Optimization
Presented by: Carlos Zetina

Description

In recent years, the scope and responsibilities of optimization Data/Decision Science (DS) teams continue to expand beyond simple model formulation. DS teams are now closely involved in the deployment, monitoring, and maintenance of the optimization models they develop.

In this session, we’ll explore how FICO Xpress Optimization facilitates collaboration among DS teams, engineers, and business users as they build end-to-end enterprise solutions using the latest technology and advances in the field. We’ll also demonstrate how FICO Xpress accelerates “time-to-value” with its state-of-the-art solver, robust modeling and programming interfaces, and its low-code/no-code application development and deployment platform.

3-4:45pm

3-4:45pm

3-4:45pm

No-code Machine Learning with JMP Student Edition and JMP Pro
Presented by: Ross Metusalem

Description
You don’t have to write code to build modern, powerful machine learning models. JMP Student Edition (free for academic use) and JMP Pro are desktop software applications that provide an interactive, point-and-click interface for predictive modeling, clustering, and dimensionality reduction, as well as specialized techniques including image classification and text mining.

The first hour of this workshop will focus on predictive modeling. You’ll learn how to use JMP software to:

Build predictive models using neural nets, tree-based methods, and other algorithms.

Perform automated cross-validation and hyperparameter tuning.

Explore models with interactive graphics.

Easily fit and compare multiple competing models.

Deploy models in JMP and other software environments.

The remainder of this workshop will highlight JMP tools for a selection of additional techniques:

Clustering,

Image classification, and

Integrating JMP and Python to combine capabilities from both.

Learn more about JMP software capabilities at jmp.com/capabilities, and get full-featured, free JMP software for academic use at jmp.com/student.

Exhibitor Workshops

Sunday, April 12

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