BBPH: using progressive hedging within branch and bound to solve multi-stage stochastic mixed integer programs

DOI10.1016/j.orl.2016.11.006zbMath1409.90119OpenAlexW2554164096MaRDI QIDQ1727944

Jason Barnett, David L. Woodruff, Jean-Paul Watson

Publication date: 21 February 2019

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.orl.2016.11.006

zbMATH Keywords

stochastic programmingbranch and boundprogressive hedging

Mathematics Subject Classification ID

Mixed integer programming (90C11) Stochastic programming (90C15)

Related Items

State-Variable Modeling for a Class of Two-Stage Stochastic Optimization Problems, Lagrange dual bound computation for stochastic service network design, Resilient route design for collection of material from suppliers with split deliveries and stochastic demands, A massively parallel interior-point solver for LPs with generalized arrowhead structure, and applications to energy system models, A progressive hedging based branch-and-bound algorithm for mixed-integer stochastic programs, A parallelized variable fixing process for solving multistage stochastic programs with progressive hedging

Uses Software

• Pyomo
• PySP

Cites Work

• Obtaining lower bounds from the progressive hedging algorithm for stochastic mixed-integer programs
• Scenario grouping in a progressive hedging-based meta-heuristic for stochastic network design
• Progressive hedging innovations for a class of stochastic mixed-integer resource allocation problems
• BFC-MSMIP: an exact branch-and-fix coordination approach for solving multistage stochastic mixed 0-1 problems
• Solving stochastic transportation network protection problems using the progressive hedging-based method
• Using scenario trees and progressive hedging for stochastic inventory routing problems
• Dual decomposition in stochastic integer programming
• Probabilistic programming with discrete distributions and precedence constrained knapsack polyhedra
• PySP: modeling and solving stochastic programs in Python
• A scenario decomposition algorithm for 0-1 stochastic programs
• Scalable Heuristics for a Class of Chance-Constrained Stochastic Programs
• Progressive hedging-based metaheuristics for stochastic network design
• Scenarios and Policy Aggregation in Optimization Under Uncertainty