Lifting Techniques For Mixed Integer Programming

Richard, Jean‐Philippe P.

doi:10.1002/9780470400531.eorms0464

Cited by 6 publications

(1 citation statement)

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“…Motivated by Proposition 1, we consider restrictions of S n,m where some variables x i are fixed either at 0 or w ij for some j ∈ M , and then projected out of the description. These restrictions of S n,m have fewer variables, which simplifies the derivation of facet-defining seed inequalities for lifting; see [27] for a survey of lifting results in MIP.…”

Section: Lifting Resultsmentioning

confidence: 99%

Network Models with Unsplittable Node Flows with Application to Unit Train Scheduling

Davarnia

Richard

Içyüz-Ay

et al. 2019

Operations Research

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We study network models where flows cannot be split or merged when passing through certain nodes, i.e., for such nodes, each incoming arc flow must be matched to an outgoing arc flow of identical value. This requirement, which we call no-split no-merge (NSNM), appears in railroad applications where train compositions can only be modified at yards where necessary equipment is available. This combinatorial requirement is crucial when formulating problems occurring in the unit train business. We propose modeling approaches to represent the NSNM requirement. In particular, we give a linear formulation of the requirement on a single node that describes the convex hull in a lifted space. We present a cut-generating linear program to obtain valid inequalities in the original space of variables, and introduce a polynomial-time procedure to lift strong inequalities of lower-dimensional models into strong inequalities of the original model. In addition, we identify an exponential family of facet-defining inequalities that can be separated efficiently. To evaluate our results computationally, we study a stylized unit train problem. We compare a solution approach based on our results with one that relies on column generation. We then show that our results significantly reduce relaxation times and gaps when compared to leading commercial branch-and-cut software.

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Section: Lifting Resultsmentioning

confidence: 99%