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Generating Alternative Mixed-Integer Programming Models Using Variable Redefinition

Author

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  • R. Kipp Martin

    (University of Chicago, Chicago, Illinois)

Abstract

Dropping the “complicating” constraints in a mixed-integer linear program often yields a “special structure subproblem” that can be reformulated using a different set of decision variables. Once the new variables have been identified, the entire problem can be reformulated in terms of the new variables. We develop a theory of variable redefinition based on relating the two sets of decision variables by a linear transformation, and describe methods for reformulating the special structure problem. The reformulated models have a more accurate linear relaxation than the problems from which they were derived, an important property within the context of linear programming-based branch-and-bound modeling approaches.

Suggested Citation

  • R. Kipp Martin, 1987. "Generating Alternative Mixed-Integer Programming Models Using Variable Redefinition," Operations Research, INFORMS, vol. 35(6), pages 820-831, December.
    • Handle: RePEc:inm:oropre:v:35:y:1987:i:6:p:820-831
    DOI: 10.1287/opre.35.6.820
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    Cited by:

    1. Minjiao Zhang & Simge Küçükyavuz & Hande Yaman, 2012. "A Polyhedral Study of Multiechelon Lot Sizing with Intermediate Demands," Operations Research, INFORMS, vol. 60(4), pages 918-935, August.
    2. S. Rajagopalan, 1998. "Capacity Expansion and Equipment Replacement: A Unified Approach," Operations Research, INFORMS, vol. 46(6), pages 846-857, December.
    3. Pedro Gazmuri & Sergio Maturana, 2001. "Developing and Implementing a Production Planning DSS for CTI Using Structured Modeling," Interfaces, INFORMS, vol. 31(4), pages 22-36, August.
    4. Awi Federgruen & Joern Meissner & Michal Tzur, 2007. "Progressive Interval Heuristics for Multi-Item Capacitated Lot-Sizing Problems," Operations Research, INFORMS, vol. 55(3), pages 490-502, June.
    5. Wolsey, Laurence A., 1995. "Progress with single-item lot-sizing," European Journal of Operational Research, Elsevier, vol. 86(3), pages 395-401, November.
    6. Amin Hosseininasab & Willem-Jan van Hoeve, 2021. "Exact Multiple Sequence Alignment by Synchronized Decision Diagrams," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 721-738, May.
    7. François Vanderbeck, 2000. "On Dantzig-Wolfe Decomposition in Integer Programming and ways to Perform Branching in a Branch-and-Price Algorithm," Operations Research, INFORMS, vol. 48(1), pages 111-128, February.
    8. Luis Gouveia, 1998. "Using Variable Redefinition for Computing Lower Bounds for Minimum Spanning and Steiner Trees with Hop Constraints," INFORMS Journal on Computing, INFORMS, vol. 10(2), pages 180-188, May.
    9. VAN VYVE, Mathieu, 2011. "Linear prices for non-convex electricity markets: models and algorithms," LIDAM Discussion Papers CORE 2011050, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    10. Crema, Alejandro, 1997. "A contraction algorithm for the multiparametric integer linear programming problem," European Journal of Operational Research, Elsevier, vol. 101(1), pages 130-139, August.
    11. Margarita P. Castro & Andre A. Cire & J. Christopher Beck, 2022. "Decision Diagrams for Discrete Optimization: A Survey of Recent Advances," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2271-2295, July.
    12. Zeger Degraeve & Raf Jans, 2007. "A New Dantzig-Wolfe Reformulation and Branch-and-Price Algorithm for the Capacitated Lot-Sizing Problem with Setup Times," Operations Research, INFORMS, vol. 55(5), pages 909-920, October.
    13. Gouveia, Luis & Requejo, Cristina, 2001. "A new Lagrangean relaxation approach for the hop-constrained minimum spanning tree problem," European Journal of Operational Research, Elsevier, vol. 132(3), pages 539-552, August.
    14. Jans, Raf, 2010. "Classification of Dantzig-Wolfe reformulations for binary mixed integer programming problems," European Journal of Operational Research, Elsevier, vol. 204(2), pages 251-254, July.
    15. Selvaprabu Nadarajah & Andre A. Cire, 2020. "Network-Based Approximate Linear Programming for Discrete Optimization," Operations Research, INFORMS, vol. 68(6), pages 1767-1786, November.

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