Skip to main content
Log in

A discussion of scalarization techniques for multiple objective integer programming

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

In this paper we consider solution methods for multiobjective integer programming (MOIP) problems based on scalarization. We define the MOIP, discuss some common scalarizations, and provide a general formulation that encompasses most scalarizations that have been applied in the MOIP context as special cases. We show that these methods suffer some drawbacks by either only being able to find supported efficient solutions or introducing constraints that can make the computational effort to solve the scalarization prohibitive. We show that Lagrangian duality applied to the general scalarization does not remedy the situation. We also introduce a new scalarization technique, the method of elastic constraints, which is shown to be able to find all efficient solutions and overcome the computational burden of the scalarizations that use constraints on objective values. Finally, we present some results from an application in airline crew scheduling as evidence.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Benson, H.P. (1978). “Existence of Efficient Solutions for Vector Maximization Problems.” Journal of Optimization Theory and Applications, 26(4), 569–580.

    Article  Google Scholar 

  • Bitran, G.R. (1977). “Linear Multiple Objective Programs with Zero-One Variables.” Mathematical Programming, 13, 121–139.

    Article  Google Scholar 

  • Bitran, G.R. (1979). “Theory and Algorithms for Linear Multiple Objective Programs with Zero-One Variables.” Mathematical Programming, 17, 362–390.

    Article  Google Scholar 

  • Butchers, E.R., P.R. Day, A.P. Goldie, S. Miller, J.A. Meyer, D.M. Ryan, A.C. Scott, and C.A. Wallace. (2001). “Optimised Crew Scheduling at Air New Zealand.” Interfaces, 31(1), 30–56.

    Article  Google Scholar 

  • Camerini, P. and C. Vercellis. (1984). “The Matroidal Knapsack: A Class of (Often) Well-Solvable Problems.” Operations Research Letters, 3(3), 157–162.

    Article  Google Scholar 

  • Carlyle, W.M. and R.K. Wood. (2003). “Lagrangian Relaxation and Enumeration for Solving Constrained Shortest Path Problems.” In L. Foulds (Ed.), Proceedings of the 38th Annual Conference of the Operational Research Society of New Zealand, pp. 3–12. Operational Research Society of New Zealand, Auckland.

  • Chankong, V. and Y.Y. Haimes. (1983). Multiobjective Decision Making Theory and Methodology. New York: Elsevier Science.

    Google Scholar 

  • Chung, S., H.W. Hamacher, F. Maffioli, and K.G. Murty. (1993). “Note on Combinatorial Optimization with Max-Linear Objective Functions.” Discrete Applied Mathematics, 42, 139–145.

    Article  Google Scholar 

  • Ecker, H.G. and N.S. Hegner. (1978). “On Computing an Initial Efficient Extreme Point.” Journal of the Operational Research Society, 29(10), 1005–1007.

    Article  Google Scholar 

  • Ecker, J.G. and I.A. Kouada. (1975). “Finding Efficient Points for Linear Multiple Objective Programs.” Mathematical Programming, 8, 375–377.

    Article  Google Scholar 

  • Ehrgott, M. and X. Gandibleux. (2000). “A Survey and Annotated Bibliography of Multiobjective Combinatorial Optimization.” OR Spektrum, 22, 425–460.

    Google Scholar 

  • Ehrgott, M. and X. Gandibleux (Eds.). (2002). Multiple Criteria Optimization—State of the Art Annotated Bibliographic Surveys, volume 52 of Kluwer's International Series in Operations Research and Management Science. Boston, MA: Kluwer Academic Publishers.

    Google Scholar 

  • Ehrgott, M. and D.M. Ryan. (2002). “Constructing Robust Crew Schedules with Bicriteria Optimization.” Journal of Multi-Criteria Decision Analysis, 11(3), 139–150.

    Article  Google Scholar 

  • Ehrgott, M. and D.M. Ryan. (2003). “The Method of Elastic Constraints for Multiobjective Combinatorial Optimization and its Application in Airline Crew Scheduling.” In T. Tanino, T. Tanaka, and M. Inuiguchi, (Eds.), Multi-Objective Programming and Goal-Programming, pp. 117–122. Berlin: Springer Verlag.

    Google Scholar 

  • Gardiner, L.R. and R.E. Steuer. (1994). “Unified Interactive Multiple Objective Programming.” European Journal of Operational Research, 74, 391–406.

    Article  Google Scholar 

  • Garey, M.R. and D.S. Johnson. (1979). Computers and Intractability—A Guide to the Theory of NP-Completeness. Freeman, San Francisco, CA.

    Google Scholar 

  • Guddat, J., F. Guerra Vasquez, K. Tammer, and K. Wendler. (1985). Multiobjective and Stochastic Optimization Based on Parametric Optimization. Berlin: Akademie-Verlag.

    Google Scholar 

  • Klamroth, K., J. Tind, and S. Zust. (2004). “Integer Programming Duality in Multiple Objective Programming.” Journal of Global Optimization, To appear.

  • Kouvelis, P. and G. Yu. (1997). Robust Discrete Optimization and Its Applications, volume 14 of Nonconvex Optimization and its Applications. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Mavrotas, G. and D. Diakoulaki. (1998). “A Branch and Bound Algorithm for Mixed Zero-One Multiple Objective Linear Programming.” European Journal of Operational Research, 107(3), 530–541.

    Article  Google Scholar 

  • Steuer, R.E. and E.U. Choo. (1983). “An Interactive Weighted Tchebycheff Procedure for Multiple Objective Programming.” Mathematical Programming, 26, 326–344.

    Article  Google Scholar 

  • Visáe, M., J. Teghem, M. Pirlot, and E.L. Ulungu (1998). “Two-Phases Method and Branch-and-Bound Procedures to Solve the Bi-Objective Knapsack Problem.” Journal of Global Optimization, 12, 139–155.

    Article  Google Scholar 

  • Warburton, A. (1987). “Approximation of Pareto Optima in Multiple-Objective Shortest-Path Problems.” Operations Research, 35(1), 70 –79.

    Article  Google Scholar 

  • Wierzbicki, A.P. (1986). “A Methodological Approach to Comparing Parametric Characterizations of Efficient Solutions.” In G. Fandel, M. Grauer, A. Kurzhanski, and A.P. Wierzbicki (Eds.), Large-Scale Modelling and Interactive Decision Analysis, volume 273 of Lecture Notes in Economics and Mathematical Systems, pp. 27–45. Berlin: Springer Verlag.

    Google Scholar 

  • Wierzbicki, A.P. (1986). “On the Completeness and Constructiveness of Parametric Characterizations to Vector Optimization Problems.” OR Spektrum, 8(2), 73–87.

    Article  Google Scholar 

  • Wierzbicki, A.P., M. Makowski, and J. Wessels (Eds.). (2000). Model-Based Decision Support Methodology with Environmental Applications. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Matthias Ehrgott.

Additional information

This research is partially supported by University of Auckland grant 3602178/9275 and by the Deutsche Forschungsgemeinschaft grant Ka 477/27-1.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ehrgott, M. A discussion of scalarization techniques for multiple objective integer programming. Ann Oper Res 147, 343–360 (2006). https://doi.org/10.1007/s10479-006-0074-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-006-0074-z

Keywords

Navigation