Bi-Parameterized Two-Stage Stochastic Min-Max and Min-Min Mixed Integer Programs

TL;DR

This paper introduces efficient Lagrangian-based methods for solving complex two-stage stochastic min-max and min-min integer programs with bi-parameterized recourse, demonstrating significant computational improvements over existing approaches.

Contribution

The paper develops Lagrangian-integrated $L$-shaped ($L^2$) methods for bi-parameterized stochastic programs, including regularization for mixed-binary cases, and demonstrates their effectiveness through computational experiments.

Findings

$L^2$ method solves all instances in 23 seconds on average.

$L^2$ method is 18.4 times faster than benchmark in facility location.

Addresses distributionally robust optimization with decision-dependent ambiguity sets.

Abstract

We introduce two-stage stochastic min-max and min-min integer programs with bi-parameterized recourse (BTSPs), where the first-stage decisions affect both the objective function and the feasible region of the second-stage problem. To solve these programs efficiently, we introduce Lagrangian-integrated $L$-shaped ($L^2$) methods, which guarantee exact solutions when the first-stage decisions are pure binary. For mixed-binary first-stage programs, we present a regularization-augmented variant of this method. Our computational results for a stochastic network interdiction problem show that the $L^2$ method outperforms a benchmark method, solving all instances in 23 seconds on average, while the benchmark method failed to solve any instance within 3600 seconds. The $L^2$ method also achieves optimal solutions, on average, 18.4 times faster for a stochastic facility location problem. Furthermore, we show that the $L^2$ method can effectively address distributionally robust optimization problems with decision-dependent ambiguity sets that may be empty for some first-stage decisions, achieving optimal solutions, on average, 5.3 times faster than existing methods.