Scenario-based learning for stochastic combinatorial optimisation

DOI10.1007/978-3-319-59776-8_23zbMath1492.90147OpenAlexW2619859405MaRDI QIDQ2011603

Guido Tack, David Hemmi, Mark G. Wallace

Publication date: 4 August 2017

Full work available at URL: https://doi.org/10.1007/978-3-319-59776-8_23

zbMATH Keywords

decomposition approachgeneric method

Mathematics Subject Classification ID

Stochastic programming (90C15) Combinatorial optimization (90C27)

Uses Software

• VRP
• MiniZinc
• CHUFFED
• Essence

Cites Work

• 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
• Stochastic constraint programming: A scenario-based approach
• Essence: A constraint language for specifying combinatorial problems
• Using scenario trees and progressive hedging for stochastic inventory routing problems
• Dual decomposition in stochastic integer programming
• Propagation via lazy clause generation
• Routing and scheduling in a flexible job shop by tabu search
• A scenario decomposition algorithm for 0-1 stochastic programs
• Exact solutions to a class of stochastic generalized assignment problems
• Vehicle Routing
• Introduction to Stochastic Programming
• Scenarios and Policy Aggregation in Optimization Under Uncertainty
• Stochastic Covering and Adaptivity