Adaptive Scenario Partitioning for Stochastic Bilevel Linear Programs

Network Relaxations for Combinatorial Bilevel Optimization Under Linear Interactions

A new representation for problems in which leader-follower interactions are captured by linear inequalities involving binary leader decisions is introduced, which uses a layered decision-diagram network whose paths encode the leader’s choices and whose terminal values represent the follower’s optimal objective value.

Dynamic Programming For Data Independent Decision Sets

The contribution is a proof that there exist measurable versions of intermediate value functions, which reveals regularity in addition, and a verification theorems allow stating traditional problem specifications in the novel setting with mathematical rigor.

Single-Loop Gradient Algorithms for Pessimistic Bilevel Optimization Problems

This work proposes a smooth approximation of PBO through reformulation, penalization and regularization, and establishes convergence guarantees in terms of both minimizers and stationarity, and develops two single-loop algorithms for deterministic and stochastic PBOs, respectively.

Generalizing single-level relaxations for bilevel linear programs

Exact and Heuristic Methods for $\Gamma$-Robust Min-Max Problems

The results show that the heuristic closes the optimality gap for a significant portion of the considered instances and often practically outperforms both heuristic and exact benchmark approaches.

Sim-approaches for the stochastic clustered team orienteering problem

A Simheuristic method integrating Monte Carlo simulations within an Adaptive Large Neighborhood Search (SimALNS) to address the Stochastic Clustered Team Orienteering Problem (SCTOP), a variant of the recently defined CluTOP where travel times are non-negative random variables.