Optimization Direct has partnered with and entered into a distribution agreement with FICO. Combining the founders’ industry and software experience and FICO’s Xpress product with the arsenal of Optimization modeling and solving tools from FICO provides customers with the most powerful capabilities in the industry. FICO Xpress solves large-scale optimization problems and enables better business decisions…

HARRINGTON PARK Nj. & BEAVERTON, Ore. October 7 2022. During the last 20 years, Mixed Integer Programming (MIP) problems have become more complex, larger, and equally complex to run. ODHeuristics (ODH) is a new algorithm created by Optimization Direct, designed to run on modern multiprocessor machines. Many cores (24+ ideal) are exploited by the ODH engine…

We are holding technology tutorials and workshops at INFORMS Technology Tutorial at INFORMS ODH Python Primer Presented by: Robert Ashford Monday, October 17, 11:40am-12:15pm   This short tutorial shows participants how to build a basic model using the ODH|CPLEX in Python. This session includes setting the Python environment, reading data from a csv or spreadsheet,…

Political redistricting through mathematical optimization.

Political redistricting can now be completed quickly, fairly and economically through mathematical optimization.

A webinar featuring Optimization Direct Experts. In Partnership with AIMMS. Replay now available.

Article five of five In our first article we identified four scenarios where Machine Learning can cooperate with Mathematical Optimization. Here we identify further reading of two notable resources that can help us learn more about this topic. We encourage practitioners to review these two articles. Machine learning for combinatorial optimization: A methodological tour d’horizon…

Article four of five In our first article we identified four scenarios where Machine Learning can cooperate with Mathematical Optimization. In this article, we will consider Scenario D which is defined as follows: ML can be used to help MOPT solvers to perform better not only for finding solutions faster but for finding more good…

Article three of five In our first article we identified four scenarios where Machine Learning can cooperate with Mathematical Optimization. In this article, we will consider Scenario A and present an example. Scenario A is defined as: ‘The output of ML is input to MOPT.’ This scenario is defined as: Machine learning algorithms calculate the…

Article two of five Alkiviadis Vazacopoulos In our first article we identified four scenarios where Machine Learning can cooperate with Mathematical Optimization. In this article, we will consider Scenario D and present promising results from a real-world application. This scenario is defined as: ML can be used to help MOPT solvers to perform better not…