Integrative Population Analysis for Better Solutions to Large-Scale Mathematical Programs

  • Fred Glover
  • John Mulvey
  • Dawei Bai
  • Michael T. Tapia
Part of the Applied Optimization book series (APOP, volume 16)


Integrative Population Analysis unties the learning process called target analysis and a generalized form of sensitivity analysis to yield improved approaches for optimization, particularly where problems from a particular domain must be solved repeatedly. The resulting framework introduces an adaptive design for mapping problems to groups, as a basis for identifying processes that permit problems within a given group to be solved more effectively. We focus in this paper on processes embodied in parameter-based definitions of regionality,accompanied by decision rules that extract representative solutions from given regions in order to yield effective advanced starting points for our solution methods. Applied to several industrial areas, our approach generates regions and representations that give an order of magnitude improvement in the time required to solve new problems that enter the population and therefore makes the application of large scale optimization models practical in reality.


Data Envelopment Analysis Model Artificial Agent Efficient Frontier Representative Agent Indifference Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 1998

Authors and Affiliations

  • Fred Glover
    • 1
  • John Mulvey
    • 2
  • Dawei Bai
    • 2
  • Michael T. Tapia
    • 3
  1. 1.The University of Colorado at BoulderBoulderUSA
  2. 2.Department of Civil Engineering and Operations ResearchPrinceton UniversityPrincetonUSA
  3. 3.Department of Management Science and Information SystemsThe University of Texas at AustinAustinUSA

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