Skip to main content

A Principled Approach to Mixed Integer/Linear Problem Formulation

  • Conference paper
Operations Research and Cyber-Infrastructure

Part of the book series: Operations Research/Computer Science Interfaces ((ORCS,volume 47))

Abstract

We view mixed integer/linear problem formulation as a process of identifying disjunctive and knapsack constraints in a problem and converting them to mixed integer form. We show through a series of examples that following this process can yield mixed integer models that automatically incorporate some of the modeling devices that have been discovered over the years for making the formulation tighter. In one case it substantially improves on the generally accepted model. We provide a theoretical basis for the process by generalizing Jeroslow’s mixed integer representability theorem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Aardal K (1998) Reformulation of capacitated facility location problems: How redundant information can help. Annals of Operations Research 82:289-309

    Article  MathSciNet  MATH  Google Scholar 

  • Beaumont N (1990) An algorithm for disjunctive programs. European Journal of Operational Research 48:362-371

    Article  MATH  Google Scholar 

  • Hooker JN (2007) Integrated Methods for Optimization. Springer, New York

    MATH  Google Scholar 

  • Jeroslow RG (1987) Representability in mixed integer programming, I: Characterization results. Discrete Applied Mathematics 17:223-243

    Article  MathSciNet  MATH  Google Scholar 

  • Jeroslow RG (1989) Logic-Based Decision Support: Mixed Integer Model Formulation. Annals of Discrete Mathematics, North-Holland

    MATH  Google Scholar 

  • Trick M (2005) Formulations and reformulations in integer programming. In: Barták R, Milano M (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2005), Springer, Lecture Notes in Computer Science, vol 3524, pp 366-379

    Chapter  Google Scholar 

  • Williams HP (1999) Model Building in Mathematical Programming, 4th Ed. Wiley, New York

    MATH  Google Scholar 

  • Williams HP (to appear) Logic and Integer Programming. Springer

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this paper

Cite this paper

Hooker, J.N. (2009). A Principled Approach to Mixed Integer/Linear Problem Formulation. In: Chinneck, J.W., Kristjansson, B., Saltzman, M.J. (eds) Operations Research and Cyber-Infrastructure. Operations Research/Computer Science Interfaces, vol 47. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-88843-9_5

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-88843-9_5

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-88842-2

  • Online ISBN: 978-0-387-88843-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics