11am-12:45pm
Room 201
Quantum Computing for Optimization
Presented by: Catherine Potts
- An introduction to quantum computers, quantum annealing, and optimization solvers
- What kinds of business problems are good candidates for quantum/ hybrid solutions
- Use case examples: how quantum hybrid is being used for optimization applications such as resource and workforce scheduling, cargo loading and other complex business problems
- How quantum systems are programmed, and the tools for developers. This will include live demos and an introduction to Leap, D-Wave’s real-time cloud service. Leap provides access to a portfolio of hybrid solvers, enabling enterprises to address all kinds of business problems that range in size and complexity.
- How to get started with quantum for business.
11am-12:45pm
Room 202
Democratizing Algorithm Deployment for Planning & Scheduling
Presented by: Justin Clark
Many organizations struggle to bridge the gap between algorithm development and real-world business applications, limiting the impact of optimization and AI-driven decision-making. This session explores how to streamline the transition from prototype to production using your own or open-source algorithms.
With the DB Gene Platform as an accelerator, teams can focus on solving business challenges rather than managing infrastructure or building UIs. Through real-world examples, we’ll demonstrate how organizations can rapidly deploy custom-fit, interactive applications, making advanced optimization intuitive, actionable, and seamlessly integrated into enterprise decision-making—without requiring software development skills.
11am-12:45pm
Room 203
GAMSPy for Fast and Flexible Optimization in Python
Presented by: Adam Christensen and Steven Dirkse
Join us for a workshop that introduces GAMSPy — a cutting-edge Python package that captures the speed and expressive power of set-based algebraic modeling languages while leveraging Python’s familiar syntax and robust and flexible ecosystem. Participants will learn how GAMSPy overcomes the challenges posed by conventional object-oriented frameworks to ensure both performance and clarity.
Throughout the workshop, we will explore:
- The Foundations of Algebraic Modeling Languages (AMLs): Understand how AMLs – like GAMSPy – lead to clearer code, better maintainability, and a shorter develop-debug-deploy cycle for your team.
- Model Building: We will work through real-world inspired examples — from data ingestion and cleaning to constructing and solving optimization models with GAMSPy.
- Machine Learning (ML) in GAMSPy: Embedding ML models into optimization problems is an exciting opportunity to create surrogate models to complex systems. Learn how to train, extract, and embed these surrogate systems into a real-world inspired example using new tools being offered in GAMSPy.
- Modern Data Pipelines in Python: Learn how to integrate and clean data for use in GAMSPy models using the GAMS Transfer API as well as other GAMS data tools.
This workshop is ideal for optimization professionals, researchers, and practitioners who are excited to integrate high performance optimization models directly into their existing Python environments.
11am-12:45pm
Room 204
Demand and Last Mile Logistics Planning Using ORMAE’s tools
Presented by: Amit Garg
OptiFIT
Join us for an exclusive workshop showcasing OptiFIT, our advanced forecasting and demand planning platform. Discover how OptiFIT leverages cutting-edge machine learning algorithms to enhance accuracy, optimise inventory, and drive better business decisions.
We will walk you through the key capabilities:
- Hierarchical Forecasting – Generate accurate forecasts at multiple levels of granularity.
- Automatic Model Selection – Let OptiFIT choose the best-performing model for your data.
- Ensemble Model Selection – Improve forecast reliability with a blend of multiple models.
- Scalability & Automation – Handle complex forecasting needs with minimal manual intervention.
We will demonstrate OptiFIT’s core functionalities and dive into the technical details behind its powerful performance.
Don’t miss this opportunity to explore the future of demand planning!
RouteCap
RouteCap is a powerful engine for logistics optimization. The optimized plans generated adhere to time, zone, manpower and vehicle constraints as per your business operations requirement while balancing workload, minimizing the number of resources utilized and maximizing savings.
If you are an organization that needs to plan customer delivery or visits across a geography on a regular basis, RouteCap can help you. In this workshop, you will experience power of math modelling to generate optimal routes for your trucks to fulfil orders.
11am-12:45pm
Room 205
SAS Analytics and AI
Presented by: Rob Pratt and Yan Xu
SAS offers extensive analytic and AI capabilities, including machine learning, deep learning, natural language processing, statistical analysis, optimization, and simulation. SAS analytic and AI 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. SAS Analytics is equipped with AI-enabled automations and modern low-code or no-code user interfaces that democratize data science usage in your organization and offer unparalleled speed to value.
We will first review the SAS analytics and AI portfolio, then highlight recently added optimization features (including automated Benders decomposition and support for Pyomo and PuLP), and finally explore several case studies in analytics and AI with a focus on optimization, deep learning and computer vision.
1-2:45pm
Room 201
The Seeker Optimization Solver
Presented by: Meinolf Sellmann
Learn how to create business-changing models using the revolutionary new solver Seeker! From lightning-fast discrete optimization, to non-hierarchical multi-objective, to non-convex, to stochastic and distributed optimization. You will learn hands-on how easy it is to create models that defy traditional solver technology with Seeker. Please bring your laptop!
1-2:45pm
Room 202
Data to Decisions, Faster and Smarter: Analytics Transformation with JMP
Presented by: Muralidhara Anandamurthy
In today’s data-driven world, the ability to swiftly transform insights into impactful decisions is a true game-changer. JMP empowers organizations to navigate this journey with speed, precision, and intelligence — offering a robust suite of tools for data exploration, predictive modeling, and statistical discovery.
By seamlessly bridging the gap between complex data and actionable insights, JMP drives analytics transformation, enabling faster, smarter, and more confident decision-making. Whether it’s uncovering hidden patterns or optimizing processes, JMP turns your data into a powerful strategic asset.
This session will showcase an engaging overview and live demonstration of JMP’s diverse analytical capabilities, from data exploration and visualization to advanced analytics highlighting its role in effective problem solving and innovation. We will cover multivariate analysis, predictive modeling and machine learning, text mining, model comparison and deployment.
1-2:45pm
Room 203
Run, Test, Replay, Let It Flow: Applying DecisionOps or Better Decision Modeling, Explainability, Reproducibility, and Stakeholder Buy-In
Presented by: Nicole Misek
Why did the optimization model pick this solution vs. another? How does the decision model perform under this range of conditions? How do I compare my plan to what the model chose? What is the best way to reproduce past model runs? Questions such as these are inherent to the journey to build, deploy, and advocate for decision models.
This session will cover selected discussion points that arise throughout the decision model lifecycle that are often challenging to address using traditional approaches. With DecisionOps, we’ll explore how more efficient methods for running decision models on remote infrastructure, simulating “what if” scenarios, reproducing past runs with a streamlined “replay mode”, setting up decision pipelines to automate workflows, and drive cohesive collaboration to catalyze stakeholder buy-in.
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, Jupyter Notebooks, Plotly, Statsmodels, Prophet, and more.
1-2:45pm
Room 204
Generative AI-Enhanced Mathematical Programming Workshop with
FICO Xpress
Presented by: Alkis Vazacopoulos
This workshop introduces participants to the transformative potential of generative AI in mathematical programming using FICO Xpress. Through hands-on demonstrations and practical exercises, attendees will learn how to leverage generative AI to streamline model development, enhance code generation, and improve result interpretation. The workshop covers key aspects including automated model formulation, intelligent debugging assistance, and AI-powered console output analysis. Participants will gain practical experience in using generative AI to solve complex optimization problems more efficiently, interpret solver outputs more effectively, and generate comprehensive documentation. This workshop is designed for optimization practitioners, researchers, and developers who want to enhance their mathematical programming workflow with state-of-the-art AI capabilities.
1-2:45pm
Room 205
Data-First Best Practices and Integrations with Princeton and Databricks
Presented by: Irv Lustig and Siddhesh Pore
Many O.R. and Machine Learning textbooks and guides focus on the models and algorithms. Automated machine learning software assumes that your data is in an easily consumable format. Optimization software providers often give examples assuming that data is organized in data structures appropriate for modeling languages or programming idioms. In the real world, data is messy, and this workshop will address how to handle this challenge and achieve outstanding results.
Significant work is required to organize and understand data and to successfully deliver advanced analytics solutions. Irv Lustig will discuss a “Data-First” approach and, using examples, illustrate best practices for organizing data for machine learning and optimization with considerations for deployment in applications. Irv will also discuss the pitfalls that can occur by not taking numerous data-related issues into account when developing an application.
Siddhesh (Sid) Pore will then discuss how Databricks’s Data Intelligence platform enables building optimization techniques on the platform. Sid will present an overview of Databricks and its ability to handle large-scale data processing and analytics. Sid will delve into the integration of solvers within the Databricks environment, discussing various implementation options and best practices. The core of the discussion will focus on how Databricks can be used to prepare data, build optimization models, and efficiently solve complex decision problems at scale. Sid will cover the process of integrating solvers, managing data flows, and visualizing results.
Commercial-grade solvers like Gurobi, along with data and analytics platforms like Databricks, are increasingly being used by businesses to address optimization challenges. These platforms help prepare data inputs and turn solver outputs into actionable applications. To illustrate these concepts Sid will present a live demonstration, using Gurobi as the solver, walking step by step through integrating solves within Databricks.
3-4:45pm
Room 201
How Optimization Generates Millions: Cases from Energy, Transportation, Supply Chain, and Finance
Presented by: Bob Fourer and Filipe Brandao
Optimization has always been a key methodology of Analytics, and today it has never been easier to apply successfully. Through a series of examples and case studies, this presentation shows how recent advances in modeling, integration, and artificial intelligence have made optimization more central than ever to generating revenue and improving profitability in key business areas.
We begin by outlining a proven approach to optimization that leverages the best features of two powerful implementation environments:
- Development in AMPL’s new MP modeling framework lets you formulate optimization problems more like you think about them, while automating the complicated and error-prone transformations required for use of advanced solver algorithms.
- Deployment in amplpy integrates AMPL’s modeling advantages with Python’s vast ecosystem for data preparation, solution analysis, and visualization.
Building on the technical advantages and broad applicability of AMPL MP and amplpy, the main part of the workshop features AMPL + Python solutions in four very different optimization-intensive application areas:
- Energy: day-ahead market clearing, economic dispatch, optimal power flow
- Transportation: airline crew scheduling
- Supply chain: production optimization, network design with redundancy
- Finance: tracking error minimization
Our examples highlight how generative AI’s broad “knowledge” of Python packages speeds integration with data sources and interactive dashboards. The result is rapid development process that slashes the time and effort needed to produce a working application that’s ready for end-users.
3-4:45pm
Room 202
Turbocharge Your Optimization with the Latest Innovations in FICO® Xpress
Presented by: Dinakar Gade
Join our workshop to discover the latest innovations in Xpress Solver. Get a firsthand look at how Xpress Insight empowers business users to harness the full potential of analytical models faster than ever before. Learn about the Compute Interface, the technology that is that is designed to handle large optimization workloads. Our expert demonstrations will also show you how industry leaders are transforming their businesses using Enterprise Optimization capabilities from FICO Platform.
Experience the latest features and performance enhancements in FICO® Xpress Solver, including advances in mixed-integer linear and nonlinear optimization.
FICO® Xpress Insight is a rapid application development and deployment framework that integrates with Xpress Solver and your own analytics. We will show how you can rapidly convert Python models into complete business applications with Xpress Insight to make your analytical models available to thousands of business users. FICO® Xpress Compute Interface allows you to seamless offload and manage optimization models with a client-server architecture. Based on the same technology stack as Xpress Insight, the Compute Interface is ready to your handle your optimization workloads.
3-4:45pm
Room 203
What’s New in Gurobi 12.0: Defining, Solving, and Explaining Optimization Models and Solutions
Presented by: Alison Cozad and Xavier Nodet
This workshop will cover two key topics: the Gurobi 12.0 release and how to both build optimization models with AI and use Gurobi’s built-in tools to enhance solution explainability.
We’ll begin with a review of Gurobi 12.0, highlighting performance improvements and enhancements to the global MINLP solver, first introduced in version 11.0. A key advancement is support for compound multivariate nonlinear expressions, eliminating the need for auxiliary variables and constraints.
Next, we’ll focus on improving the entire process—from problem definition to solution explanation. We’ll start with Gurobi AI Modeling, a set of resources to help develop optimization problems and to learn modeling while navigating common pitfalls. But building a model is just the first step—understanding its solutions is just as important. We’ll explore Gurobi’s features through the lens of solution explainability, including Solution Pools, Multi-Scenario Analysis, and Infeasibility Analysis, and how they help users interpret and trust their solutions. By showing you how to use both AI-assisted modeling and tools for solution interpretability, we’ll demonstrate how to make optimization more accessible and actionable.
3-4:45pm
Room 204
Hexaly, a New Kind of Global Optimization Solver
Presented by: Fred Gardi
Hexaly is a new type of global optimization solver. Its modeling interface is nonlinear and set-oriented. It also supports user-coded functions, enabling seamless integration of simulation with optimization or machine learning with optimization. The Hexaly API unifies modeling concepts from mixed-integer programming, nonlinear programming, and constraint programming. Under the hood, Hexaly combines various exact and heuristic optimization methods: spatial branch-and-bound, simplex methods, interior-point methods, propagation, automatic branch-cut-price, local search, and surrogate modeling.
Hexaly stands out from traditional solvers like Gurobi, CPLEX, and OR-Tools by delivering super-fast solutions to problems such as routing, sequencing, scheduling, packing, clustering, matching, assignment, and location. For example, Hexaly provides solutions close to the best-known results in the literature for vehicle routing problems with thousands of points and scheduling problems with millions of tasks, achieving this in just one minute of runtime on a basic computer.
In addition to the Optimizer, Hexaly offers an innovative platform called Hexaly Studio, designed to prototype, develop, and deploy optimization applications quickly in a low-code fashion. This web-based platform is also well-suited for educational purposes; like all other Hexaly products, it is free for faculty and students.
3-4:45pm
Room 205
Using Simul8 for Real-time Decision Making with Digital Twins
Presented by: Cheryl Pammer
In this interactive session, you will take on two real-world capital expenditure (CAPEX) decisions, first using basic calculations and then refining your choices after running a simple simulation model using Simul8. This hands-on approach will highlight the risks of overestimating or underestimating investment needs and demonstrate how Discrete Event Simulation (DES) can provide data-driven insights to make more confident, cost-effective decisions.
Next, you’ve built a simulation to support a critical CAPEX decision—now, how can you leverage that investment for ongoing operational decision-making? We’ll explore how to use the same simulation to drive efficiencies, reduce costs, and support faster, data-driven choices in day-to-day operations. Through interactive examples, we’ll demonstrate how well-designed Simul8-powered Digital Twins are dynamic resources that continuously deliver value, saving time and money every time a crucial decision arises.
