A decomposition heuristic for mixed-integer supply chain problems

Schewe, Lars; Schmidt, Martin; Weninger, Dieter

doi:10.1016/j.orl.2020.02.006

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…In SCIP 7.0, the Benders decomposition framework and the heuristic Graph Induced Neighborhood Search were extended to exploit user-provided decompositions, and a first version of the heuristic Penalty Alternating Direction Method (PADM) [25,55] was introduced. SCIP 8.0 comes with an improvement of PADM and provides another decomposition heuristic Dynamic Partition Search (DPS) [8].…”

Section: Primal Decomposition Heuristicsmentioning

confidence: 99%

Enabling Research through the SCIP Optimization Suite 8.0

Bestuzheva

Besançon

Chen

et al. 2023

ACM Trans. Math. Softw.

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The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP . The focus of this paper is on the role of the SCIP Optimization Suite in supporting research. SCIP ’s main design principles are discussed, followed by a presentation of the latest performance improvements and developments in version 8.0, which serve both as examples of SCIP ’s application as a research tool and as a platform for further developments. Further, the paper gives an overview of interfaces to other programming and modeling languages, new features that expand the possibilities for user interaction with the framework, and the latest developments in several extensions built upon SCIP .

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Section: Primal Decomposition Heuristicsmentioning

confidence: 99%

Enabling Research through the SCIP Optimization Suite 8.0

Bestuzheva

Besançon

Chen

et al. 2023

ACM Trans. Math. Softw.

Self Cite

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“…Then the subproblems are solved by an alternating procedure. A detailed description of penalty alternating direction methods and their practical application can be found in Geißler et al [37] and Schewe et al [96].…”

Section: ≥-Mixing Cuts Consider the Variable Lower Bounds Of Variablementioning

confidence: 99%

The SCIP Optimization Suite 8.0

Bestuzheva¹,

Besançon²,

Chen³

et al. 2021

Preprint

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The SCIP Optimization Suite provides a collection of software packages for mathematical optimization centered around the constraint integer programming framework SCIP. This paper discusses enhancements and extensions contained in version 8.0 of the SCIP Optimization Suite. Major updates in SCIP include improvements in symmetry handling and decomposition algorithms, new cutting planes, a new plugin type for cut selection, and a complete rework of the way nonlinear constraints are handled. Additionally, SCIP 8.0 now supports interfaces for Julia as well as Matlab. Further, UG now includes a unified framework to parallelize all solvers, a utility to analyze computational experiments has been added to GCG, dual solutions can be postsolved by PaPILO, new heuristics and presolving methods were added to SCIP-SDP, and additional problem classes and major performance improvements are available in SCIP-Jack. Keywords Constraint integer programming • linear programming • mixed-integer linear programming • mixed-integer nonlinear programming • optimization solver • branch-andcut • branch-and-price • column generation • parallelization • mixed-integer semidefinite programming Mathematics Subject Classification 90C05 • 90C10 • 90C11 • 90C30 • 90C90 • 65Y05 * Extended author information is available at the end of the paper.

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“…On a more specialized testset of real-world supply chain instances, however, we have observed this presolve method to consistently produce more reductions. In Schewe et al (2020), a testset of 40 real-world supply chain instances was studied. The instances contain 330108 variables and 145450 constraints in arithmetic mean.…”

Section: Exploiting Complementary Slackness On Two Columns Of Continumentioning

confidence: 99%

Two-row and two-column mixed-integer presolve using hashing-based pairing methods

Gemander

Chen

Weninger

et al. 2020

EURO Journal on Computational Optimization

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In state-of-the-art mixed-integer programming solvers, a large array of reduction techniques are applied to simplify the problem and strengthen the model formulation before starting the actual branch-and-cut phase. Despite their mathematical simplicity, these methods can have significant impact on the solvability of a given problem. However, a crucial property for employing presolve techniques successfully is their speed. Hence, most methods inspect constraints or variables individually in order to guarantee linear complexity. In this paper, we present new hashing-based pairing mechanisms that help to overcome known performance limitations of more powerful presolve techniques that consider pairs of rows or columns. Additionally, we develop an enhancement to one of these presolve techniques by exploiting the presence of set-packing structures on binary variables in order to strengthen the resulting reductions without increasing runtime. We analyze the impact of these methods on the MIPLIB 2017 benchmark set based on an implementation in the MIP solver SCIP.

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A decomposition heuristic for mixed-integer supply chain problems

Cited by 11 publications

References 14 publications

Enabling Research through the SCIP Optimization Suite 8.0

Enabling Research through the SCIP Optimization Suite 8.0

The SCIP Optimization Suite 8.0

Two-row and two-column mixed-integer presolve using hashing-based pairing methods

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