What is it about?
Large-scale databases may not be conducive as input for solving optimization models possibly driving the computation time to unacceptable durations. Aggregation of the data, solving the optimization model with the smaller dataset, & estimating the solutions for the original database is explored.
Featured Image
Why is it important?
We are often overwhelmed with the size of datasets and there are many implications when solving optimization models. What level of data should be used? How is it best to cluster data for creating a reduced dataset? What if the size of the dataset overwhelms the capability of the solution process? How can we get the best estimates for the solution for the optimization model with the original dataset by using the reduced dataset?
Perspectives
Aggregation and disaggregation techniques will become increasingly important as we continually collect large amounts of data and wish to use it in a myriad of ways.
David F. Rogers
University of Cincinnati
Read the Original
This page is a summary of: Aggregation and Disaggregation Techniques and Methodology in Optimization, Operations Research, August 1991, INFORMS,
DOI: 10.1287/opre.39.4.553.
You can read the full text:
Resources
Contributors
The following have contributed to this page
