Abstract
The paper concerns a multiobjective heuristic to compute approximate efficient solutions for the assignment problem with two objectives. The aim here is to show that the genetic information extracted from supported solutions constitutes a useful genetic heritage to be used by crossover operators to approximate non-supported solutions. Bound sets describe one acceptable limit for applying a local search over an offspring. Results of extensive numerical experiments are reported. All exact efficient solutions are obtained using Cplex in a basic enumerative procedure. A comparison with published results shows the efficiency of this approach.
Keywords
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
M. Ehrgott and X. Gandibleux, Multiobjective Combinatorial Optimization. In Multiple Criteria Optimization: State of the Art Annotated Bibliographic Survey (M. Ehrgott and X. Gandibleux Eds.), pp. 369–444, Kluwer’s International Series in Operations Research and Management Science: Volume 52, Kluwer Academic Publishers, Boston, 2002.
X. Gandibleux, H. Morita, N. Katoh, The Supported Solutions used as a Genetic Information in a Population Heuristic. In Evolutionary Multi-Criterion Optimization (E. Zitzler, K. Deb, L. Thiele, C. Coello, D. Corne Eds.). pp. 429–442, Lecture Notes in Computer Sciences 1993, Springer, 2001.
M. Ehrgott and X. Gandibleux, Bounds and Bound Sets for Biobjective Combinatorial Optimization Problems. In Multiple Criteria Decision Making in the New Millennium (M. Koksalan and St. Ziont Eds.) pp. 241–253, Lecture Notes in Economics and Mathematical Systems 507, Springer, 2001.
R. Malhotra, H.L. Bhatia and M.C. Puri, Bi-criteria assignment problem, Operations Research, 19(2): 84–96, 1982.
Ulungu-Ekunda Lukata, Optimisation Combinatoire multicritère: Détermination de l’ensemble des solutions efficaces et méthodes interactives, Université de Mons-Hainaut, Faculté des Sciences, 313 pages, 1993.
D. Tuyttens, J. Teghem, Ph. Fortemps and K. Van Nieuwenhuyse, Performance of the MOSA method for the bicriteria assignment problem, Journal of Heuristics, 6 pp. 295–310, (2000).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gandibleux, X., Morita, H., Katoh, N. (2003). Use of a Genetic Heritage for Solving the Assignment Problem with Two Objectives. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_4
Download citation
DOI: https://doi.org/10.1007/3-540-36970-8_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-01869-8
Online ISBN: 978-3-540-36970-7
eBook Packages: Springer Book Archive