Skip to main content

PolySCIP

  • Conference paper
  • First Online:
Mathematical Software – ICMS 2016 (ICMS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9725))

Included in the following conference series:

Abstract

PolySCIP [1] is a new solver for multi-criteria integer and multi-criteria linear programs handling an arbitrary number of objectives. It is available as an official part of the non-commercial constraint integer programming framework SCIP. It utilizes a lifted weight space approach to compute the set of supported extreme non-dominated points and unbounded non-dominated rays, respectively. The algorithmic approach can be summarized as follows: At the beginning an arbitrary non-dominated point is computed (or it is determined that there is none) and a weight space polyhedron created. In every next iteration a vertex of the weight space polyhedron is selected whose entries give rise to a single-objective optimization problem via a combination of the original objectives. If the optimization of this single-objective problem yields a new non-dominated point, the weight space polyhedron is updated. Otherwise another vertex of the weight space polyhedron is investigated. The algorithm finishes when all vertices of the weight space polyhedron have been investigated. The file format of PolySCIP is based on the widely used MPS format and allows a simple generation of multi-criteria models via an algebraic modelling language.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

  1. PolySCIP website: http://polyscip.zib.de

  2. PolySCIP user guide: http://polyscip.zib.de/download/userguide.pdf

  3. Ralphs, T., Guzelsoy, M., Mahajan, A.: SYMPHONY Version 5.5 User’s Manual. Lehigh University (2013). https://projects.coin-or.org/SYMPHONY

  4. Löhne, A.: Vector Optimization with Infimum and Supremum. Springer, Heidelberg (2011). www.bensolve.org

    Book  MATH  Google Scholar 

  5. Achterberg, T.: SCIP: solving constraint integer programs. Math. Program. Comput. 1(1), 1–41 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Gamrath, G., Fischer, T., Gally, T., et al.: The SCIP Optimization Suite 3.2. ZIB-Report 15–60 (2016)

    Google Scholar 

  7. SCIP Optimization Suite. http://scip.zib.de

  8. Gurobi Optimization. www.gurobi.com

  9. FICO Xpress Optimization Suite. www.fico.com/en/products/fico-xpress-optimization-suite

  10. ILOG Cplex Optimization Studio. www-03.ibm.com/software/products/en/ibmilogcpleoptistud

  11. CRC (2011) Observation of strains: Sustainable Manufacturing. www.sustainable-manufacturing.net

  12. Isermann, H.: Proper efficiency and the linear vector maximum problem. Oper. Res. 22(1), 189–191 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ehrgott, M.: Multicriteria Optimization. Springer, Heidelberg (2005)

    MATH  Google Scholar 

  14. Benson, H.P., Sun, E.: Outcome space partition of the weight set in multiobjective linear programming. J. Optim. Theory Appl. 105(1), 17–36 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  15. Przybylski, A., Gandibleux, X., Ehrgott, M.: A recursive algorithm for finding all nondominated extreme points in the outcome set of a multiobjective integer programme. INFORMS J. Comput. 22(3), 371–386 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  16. Özpeynirci, Ö., Köksalan, M.: An exact algorithm for finding extreme supported nondominated points of multiobjective mixed integer programs. Manage. Sci. 56(12), 2302–2315 (2010)

    Article  MATH  Google Scholar 

  17. Benson, H.P.: An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem. J. Global Optim. 13(1), 1–24 (1998)

    Article  MathSciNet  MATH  Google Scholar 

  18. Ehrgott, M., Löhne, A., Shao, L.: A dual variant of Benson’s “outer approximation algorithm” for multiple objective linear programming. J. Global Optim. 52(4), 757–778 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  19. LEMON Graph Library. https://lemon.cs.elte.hu/trac/lemon

  20. MPS format: http://lpsolve.sourceforge.net/5.5/mps-format.htm

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Ralf Borndörfer , Martin Skutella or Timo Strunk .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Borndörfer, R., Schenker, S., Skutella, M., Strunk, T. (2016). PolySCIP. In: Greuel, GM., Koch, T., Paule, P., Sommese, A. (eds) Mathematical Software – ICMS 2016. ICMS 2016. Lecture Notes in Computer Science(), vol 9725. Springer, Cham. https://doi.org/10.1007/978-3-319-42432-3_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42432-3_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42431-6

  • Online ISBN: 978-3-319-42432-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics