Implementing the branch-and-cut approach for a general purpose Benders' decomposition framework
type: Item
instance of: scholarly article
title: Implementing the branch-and-cut approach for a general purpose Benders' decomposition framework
zbMATH Open document ID: 1487.90487
author: Stephen J. Maher
published in: European Journal of Operational Research
publication date: 2021-06-03T00:00:00Z
Mathematics Subject Classification ID: 90C11
Mathematics Subject Classification ID: 90C57
Mathematics Subject Classification ID: 90-04
zbMATH DE Number: 7354607
zbMATH Keywords: Benders' decomposition
zbMATH Keywords: branch-and-cut
zbMATH Keywords: mixed integer programming
zbMATH Keywords: constraint integer programming
zbMATH Keywords: optimisation software
describes a project that uses: SCIP
describes a project that uses: Gurobi
describes a project that uses: ABACUS
describes a project that uses: MINTO
describes a project that uses: DSP
describes a project that uses: CHiPPS
describes a project that uses: ddsip
describes a project that uses: SYMPHONY
describes a project that uses: Pyomo
describes a project that uses: SLPTESTSET
describes a project that uses: DIP
describes a project that uses: BaPCod
describes a project that uses: PySP
describes a project that uses: ConicBundle
describes a project that uses: CPLEX
describes a project that uses: XPRESS
describes a project that uses: PySCIPOpt
describes a project that uses: SIPLIB
MaRDI profile type: Publication
full work available at URL: j.ejor.2020.08.037
OpenAlex ID: W3052995245
cites work: Q5241193
cites work: Improving the integer L-shaped method
cites work: On a new collection of stochastic linear programming test problems
cites work: Partitioning procedures for solving mixed-variables programming problems
cites work: Automatic Dantzig-Wolfe reformulation of mixed integer programs
cites work: Measuring the impact of primal heuristics
cites work: Strengthened Benders Cuts for Stochastic Integer Programs with Continuous Recourse
cites work: Combinatorial Benders' Cuts for Mixed-Integer Linear Programming
cites work: Benders Decomposition for Simultaneous Aircraft Routing and Crew Scheduling
cites work: Benchmarking optimization software with performance profiles.
cites work: An improved Benders decomposition applied to a multi-layer network design problem
cites work: Generalized Benders decomposition
cites work: Multicommodity Distribution System Design by Benders Decomposition
cites work: The ABACUS system for branch-and-cut-and-price algorithms in integer programming and combinatorial optimization
cites work: Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs
cites work: Q4735940
cites work: The integer \(L\)-shaped method for stochastic integer programs with complete recourse
cites work: On parallelizing dual decomposition in stochastic integer programming
cites work: Accelerating Benders Decomposition: Algorithmic Enhancement and Model Selection Criteria
cites work: PySCIPOpt: Mathematical Programming in Python with the SCIP Optimization Suite
cites work: Recoverable robust single day aircraft maintenance routing problem
cites work: A Modified Benders' Partitioning Algorithm for Mixed Integer Programming
cites work: A computational study of Benders decomposition for the integrated aircraft routing and crew scheduling problem
cites work: A New Scenario Decomposition Method for Large-Scale Stochastic Optimization
cites work: An interior-point Benders based branch-and-cut algorithm for mixed integer programs
cites work: MINTO, a Mixed INTeger Optimizer
cites work: Minimizing a stochastic maximum-reliability path
cites work: Practical enhancements to the Magnanti-Wong method
cites work: Integrated airline scheduling
cites work: Dantzig-Wolfe decomposition and branch-and-price solving in G12
cites work: The Benders decomposition algorithm: a literature review
cites work: Parallel branch, cut, and price for large-scale discrete optimization
cites work: A stochastic programming approach for supply chain network design under uncertainty
cites work: Q4475618
cites work: PySP: modeling and solving stochastic programs in Python
cites work: Computational experience with a software framework for parallel integer programming
cites work: A computational study of a solver system for processing two-stage stochastic LPs with enhanced Benders decomposition
DOI: 10.1016/J.EJOR.2020.08.037
description: scientific article; zbMATH DE number 7354607