Labor Strategy Optimization for the Professional Services Industry
People are the biggest asset for the professional services industry, as well as one of its biggest expenses. With the help of strategic workforce planning, a professional service firm can better allocate its available workforce to the demands of service delivery.
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Migrating from Excel-based planning tools to enterprise-ready optimization models and applications
Excel is still the most widely used tool for strategic, tactical and operational purposes in companies around the world.
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Improvements in Gurobi v8.0
Find out what’s new in Gurobi Optimizer 8.0. This latest release contains performance improvements and significant product enhancements.
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Proven Techniques for Solving Financial Problems with Gurobi
Watch this webinar, featuring FINOR, to learn how mathematical optimization models can be applied to portfolio selection.
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Latest Developments in MIP Algorithms and Applications in the Power and Energy Industries (Chinese Language)
Watch this webinar to learn about the latest developments in MIP algorithms and applications in the power & energy industries.
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Opalytics Custom Development and Cloud Deployment
Developing optimization solutions often requires rapid deployment to users in order to take full advantage of the capabilities.
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Tech Talk & Chat– Visualization Tools
In this Tech Talk, key members of the Gurobi team introduce “Grblogtools”. Learn about this tool that allows you to analyze dozens or even hundreds of Gurobi log files.
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Choosing a Math Programming Solver
Watch this webinar for tips on how to choose a mathematical programming solver to help you tackle your complex business problems.
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Mathematical Optimization and Machine Learning
Mathematical optimization and Machine Learning (ML) are different but complementary technologies. Simply put – Mixed Integer Programming (MIP) answers questions that ML cannot. Machine learning makes predictions while MIP makes decisions. For Data Scientists to be effective, an understanding of MIP and when to use it is critical, as ML does not solve all problems effectively.
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