Disjunctive Decomposition for Two-Stage Stochastic Mixed-Binary Programs with Random Recourse

Authors:
Lewis Ntaimo
Affiliations:
Department of Industrial and Systems Engineering, Texas A&M University, College Station, Texas 77843
Venue:
Operations Research
Year:
2010

Citing 12
Cited 1

Nondifferential reverse convex programs and facetial convexity cuts via a disjuntive characterization

Mathematical Programming: Series A and B
A lift-and-project cutting plane algorithm for mixed 0-1 programs

Mathematical Programming: Series A and B
Continuity properties of expectation functions in stochastic integer programming

Mathematics of Operations Research
Mixed 0-1 programming by lift-and-project in a branch-and-cut framework

Management Science
Decomposition Algorithms for Stochastic Programming on a Computational Grid

Computational Optimization and Applications
A finite branch-and-bound algorithm for two-stage stochastic integer programs

Mathematical Programming: Series A and B
The C3 Theorem and a D2 Algorithm for Large Scale Stochastic Mixed-Integer Programming: Set Convexification

Mathematical Programming: Series A and B
The Million-Variable "March" for Stochastic Combinatorial Optimization

Journal of Global Optimization
Decomposition with branch-and-cut approaches for two-stage stochastic mixed-integer programming

Mathematical Programming: Series A and B
On solving discrete two-stage stochastic programs having mixed-integer first- and second-stage variables

Mathematical Programming: Series A and B
On a stochastic sequencing and scheduling problem

Computers and Operations Research
A comparative study of decomposition algorithms for stochastic combinatorial optimization

Computational Optimization and Applications

Integrated simulation and optimization for wildfire containment

ACM Transactions on Modeling and Computer Simulation (TOMACS)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces disjunctive decomposition for two-stage mixed 0-1 stochastic integer programs (SIPs) with random recourse. Disjunctive decomposition allows for cutting planes based on disjunctive programming to be generated for each scenario subproblem under a temporal decomposition setting of the SIP problem. A new class of valid inequalities for mixed 0-1 SIP with random recourse is presented. In particular, we derive valid inequalities that allow for scenario subproblems for SIP with random recourse but deterministic technology matrix and right-hand side vector to share cut coefficients. The valid inequalities are used to derive a disjunctive decomposition method whose derivation has been motivated by real-life stochastic server location problems with random recourse, which find many applications in operations research. Computational results with large-scale instances to demonstrate the potential of the method are reported.