COVID-19 Hospital Capacity Management using Mathematical Models
In this webinar Assistant Professor Kimia Ghobadi, Department of Civil and Systems Engineering at Johns Hopkins University, discusses the mathematical models her team developed to match COVID-19 demand with available resources in a network of hospitals through patient transfer.
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How to Exploit Parallelism in Linear and Mixed-Integer Programming
Watch this webinar to learn how to exploit parallelism in linear and mixed-integer programming.
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How To Synchronize Complex Routing Operations (Synched VRPs) with Gurobi (English)
In this webinar, we present different ways to model vehicle routing problems, discuss the advantages of each modeling alternative, explain how to model the requirements related to synching resources in routing activities, assess the best modeling alternative depending on the size of the problem.
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Gurobi 8.0 – Enhancements to Instant Cloud and Compute Server
The latest Gurobi v8.0 adds in expansive new features to the Gurobi Instant Cloud and Compute Server, such as moving to standard communication protocols to help improve security and increase robustness, dynamic addition and removal of clusters, compute server monitoring and management capabilities, improved machine and pool management, and more.
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Tech Talk & Chat– Converting Weak to Strong MIP Formulations, Part II
Watch Part 2 of this Tech Talk on converting weak to strong MIP formulations.
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Gurobi 9.0 Compute Server – New Features and Enhancements
Watch this video to learn about the recent release of Gurobi 9.0 which adds a new server component, the Cluster Manager, that can be installed with your Compute Server nodes. It provides better security with user authentication and API keys and also expands the capabilities of the cluster nodes with unified management of interactive and non-interactive optimization tasks.
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[Podcast] Data Science Based Decisions: Mixed-Integer Programming
Learn how machine learning and optimization can complement each other; the former making predictions about likely future business outcomes, and the latter suggesting appropriate actions to take in order to take advantage of these outcomes.
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Integrating Machine Learning with Mathematical Optimization: Resource Matching
Large professional services companies employ thousands of experts to deliver a wide variety of services, making labor the industry’s highest expense. Current manual processes and tools used within labor resources management present many limitations leading to poor demand fulfillment, low labor resource utilization, high project delivery costs, and poor customer satisfaction.
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Optimizing to Make Better Conservation Decisions
In this webinar, Dr. Jeffrey Hanson, Postdoctoral Scientist, Carleton University, will start with an introduction to systematic conservation planning, explain some of the key concepts and mathematical problems formulations for generating conservation plans, and highlight case studies from the literature. Dr. Richard Schuster, Director of Social Planning and Innovation, Nature Conservancy of Canada, will then compare exact algorithm solvers with conventional software for conservation planning, and talk about some real-world examples where exact algorithms solvers -- such as Gurobi -- have been used to help guide conservation decisions. By using exact algorithm solvers, conservation practitioners can quickly identify cost-effective conservation plans to inform decision making.
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Python I: Introduction to Gurobi Python Modeling
Python is a powerful and well-supported programming language that’s also a good choice for mathematical modeling. It has special features that make it easy to build and maintain optimization models.
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