mixed-integer linear optimization

mixed-integer linear optimization

Advanced Analytics
Subramanian Pazhani 0
Back to School Optimization

Public and private schools are struggling to figure out how to bring face-to-face instruction to students during this pandemic. Health risks to students and teachers, parents struggling with child-care options and/or support for virtual learning, and schools’ capacities and budget limitations make this problem a severe logistical challenge. Schools need

Advanced Analytics | Data Visualization
Sertalp B. Cay 0
Visiting all 30 Major League Baseball Stadiums - with Python and SAS® Viya®

Ballpark Chasers A cross-country trip is pretty much an all-American experience, and so is baseball. Traveling around the country to see all 30 Major League Baseball (MLB) stadiums is not a new idea; there's even a social network between so-called "Ballpark Chasers" where people communicate and share their journeys. Even

Advanced Analytics
Jared Erickson 0
Creating Synthetic Data with SAS/OR

A common barrier to quantitative research, especially in health and financial areas, is the inability to share sensitive data due to confidentiality and privacy. It can be difficult and time consuming to get permission to share the data, which means useful research is delayed or not even attempted. However, collaborators seeking

Advanced Analytics
Imre Pólik 0
How good is the MILP solver in SAS/OR?

There has been a lot of speculation over the years about the quality of the optimization solvers in SAS/OR, in particular the mixed integer linear optimization (MILP) solver. Measuring the performance of optimization solvers and comparing different solvers on a test set is a crucial part of modern optimization solver development.

Advanced Analytics
Rob Pratt 0
Detecting Anomalies in the NFL Schedule

Super Bowl 50 (L?) is this Sunday, so it's time for another (American) football-related post. Steven Miller, a mathematics professor at Rutgers University, recently noted that the 2015 NFL schedule allowed a competitive advantage for some teams (including the Carolina Panthers). This figure he generated displays the 2015 regular season

Advanced Analytics
Emily Lada 0
Simulate to validate

The primary objective of many discrete-event simulation projects is system investigation.  Output data from the simulation model are used to better understand the operation of the system (whether that system is real or theoretical), as well as to conduct various "what-if"-type analyses.   However, I recently worked on another model

Advanced Analytics
Matthew Galati 0
The kidney exchange problem

Suppose someone needs a kidney transplant and a family member is willing to donate one. If the donor and recipient are incompatible (because of blood types, tissue mismatch, and so on), the transplant cannot happen. Now suppose two donor-recipient pairs A and B are in this situation, but donor A

Back to Top