Skip to main content
Log in

On the separability of subproblems in Benders decompositions

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

Abstract

Benders decomposition is a well-known procedure for solving a combinatorial optimization problem by defining it in terms of a master problem and a slave problem. Its effectiveness relies, among other factors, on the possibility of synthesizing Benders cuts that rule out not only one, but a large class of trial values for the master problem. In turn, for the class of problems we consider (i.e., optimization plus constraint satisfaction) the possibility of separating the slave problem into several subproblems—i.e., problems exhibiting strong intra-relationships and weak inter-relationships—can be exploited for improving searching procedures efficiency. The notion of separation is typically given informally, or relying on syntactical aspects. This paper formally addresses the notion of slave problem separability by giving a semantic definition and exploring it from the computational point of view. Several examples of separable problems are provided, including some proving that a semantic notion of separability is much more helpful than a syntactic one. We show that separability can be formally characterized as equivalence of logical formulae, and prove the undecidability of the separability check problem. Finally, we show how there are cases where automated tools can still be used for checking subproblem separability.

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

  • Benders, J. F. (1962). Partitioning procedures for solving mixed-variables programming problems. Numerische Mathematik, 4, 238–252.

    Article  Google Scholar 

  • Börger, E., Gräedel, E., & Gurevich, Y. (1997). The classical decision problem. Perspectives in mathematical logic. Berlin: Springer.

    Google Scholar 

  • Cadoli, M., Mancini, T., Micaletto, D., & Patrizi, F. (2006). Evaluating ASP and commercial solvers on the CSPLib. In Proceedings of the seventeenth European conference on artificial intelligence (ECAI 2006).

  • Cambazard, H., & Jussien, N. (2005). Integrating Benders decomposition within constraint programming. In Lecture notes in artificial intelligence : Vol. 3709. Proceedings of the eleventh international conference on principles and practice of constraint programming (CP 2005) (pp. 752–756). Berlin: Springer.

    Chapter  Google Scholar 

  • Dechter, R. (2003). Constraint processing. San Mateo: Morgan Kaufmann.

    Google Scholar 

  • Fagin, R. (1974). Generalized first-order spectra and polynomial-time recognizable sets. In R. M. Karp (Ed.), Complexity of computation (pp. 43–74). Providence: Am. Math. Soc.

    Google Scholar 

  • Hooker, J. (2000). Logic-based methods for optimization: combining optimization and constraint satisfaction (Chap. 19, pp. 389–422). New York: Wiley.

    Google Scholar 

  • Hooker, J. N., & Ottosson, G. (2003). Logic-based Benders decomposition. Mathematical Programming, 96, 33–60.

    Google Scholar 

  • Jain, V., & Grossmann, I. E. (2001). Algorithms for hybrid MILP/CP models for a class of optimization problems. INFORMS Journal on Computing, 13, 258–276.

    Article  Google Scholar 

  • Lau, K. F., & Dill, K. A. (1989). A lattice statistical mechanics model of the conformational and sequence spaces of proteins. Macromolecules, 22, 3986–3997.

    Article  Google Scholar 

  • Lenzerini, M. (2002). Data integration: A theoretical perspective. In Proceedings of the twentyfirst ACM SIGACT SIGMOD SIGART symposium on principles of database systems (PODS 2002) (pp. 233–246).

  • Papadimitriou, C. H. (1994). Computational complexity. Reading, MA: Addison Wesley.

    Google Scholar 

  • Puget, J. F. (1998). A fast algorithm for the bound consistency of alldiff constraints. In Proceedings of the fifteenth national conference on artificial intelligence (AAAI’98) (pp. 359–366).

  • Quaife, A. (1992). Automated development of fundamental mathematical theories. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Smith, B. M., Stergiou, K., & Walsh, T. (2000). Using auxiliary variables and implied constraints to model non-binary problems. In AAAI/IAAI (pp. 182–187).

  • Van Hentenryck, P. (1999). The OPL optimization programming language. Cambridge: MIT Press.

    Google Scholar 

  • Walsh, T. (2001). Permutation problems and channelling constraints. In Lecture notes in computer science : Vol. 2250. Proceedings of the eighth international conference on logic for programming, artificial intelligence and reasoning (LPAR 2001) (pp. 377–391). Berlin: Springer.

    Chapter  Google Scholar 

  • Wos, L. (1996). The automation of reasoning: An experimenter notebook with OTTER tutorial. San Diego: Academic Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fabio Patrizi.

Additional information

An earlier version of this paper appeared in Proc. of 3rd International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CP-AI-OR’06). Cork, Ireland. May, 2006.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cadoli, M., Patrizi, F. On the separability of subproblems in Benders decompositions. Ann Oper Res 171, 27–43 (2009). https://doi.org/10.1007/s10479-008-0383-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-008-0383-5

Keywords

Navigation