## Data-driven Prediction of Relevant Scenarios for Robust Combinatorial Optimization

We study iterative methods for (two-stage) robust combinatorial optimization problems with discrete uncertainty. We propose a machine-learning-based heuristic to determine starting scenarios that provide strong lower bounds. To this end, we design dimension-independent features and train a Random Forest Classifier on small-dimensional instances. Experiments show that our method improves the solution process for larger instances … Read more

## Γ-robust Optimization of Project Scheduling Problems

 In this paper, we investigate the problem of finding a robust baseline schedule for the project scheduling problem under uncertain process times. We assume that the probability distribution for the duration is unknown but an estimation together with an interval in which this time can vary is given. At most $\Gamma$ of … Read more

## General Polyhedral Approximation of Two-Stage Robust Linear Programming

 We consider two-stage robust linear programs with a budget of uncertainty for the righthand side. In this scenario set, which is frequently used in robust optimization, the uncertain righthand side for each row lies in an interval and the relative increases summed over all rows are less than a budget $$\Gamma$$. We develop a … Read more

## Machine Learning for K-adaptability in Two-stage Robust Optimization

Two-stage robust optimization problems constitute one of the hardest optimization problem classes.One of the solution approaches to this class of problems is K-adaptability. This approach simultaneously seeks the best partitioning of the uncertainty set of scenarios into K subsets, and optimizesdecisions corresponding to each of these subsets. In general case, it is solved using the … Read more

## Adaptive robust optimization with objective uncertainty

In this work, we study optimization problems where some cost parameters are not known at decision time and the decision flow is modeled as a two-stage process within a robust optimization setting. We address general problems in which all constraints (including those linking the first and the second stages) are defined by convex functions and … Read more

## Two-Stage Robust Telemedicine Assignment Problem with Uncertain Service Duration and No-Show Behaviours

The current pandemic of COVID-19 has caused significant strain on medical center resources, which are the main places to provide the rapid response to COVID-19 through the adoption of telemedicine. Thus healthcare managers must make an effective assignment plan for the patients and telemedical doctors when providing telemedicine services. Motivated by this, we present the … Read more

## Reliable p-median facility location problem: two-stage robust models and algorithms

In this paper, we propose a set of two-stage robust optimization models to design reliable p-median facility location networks subject to disruptions. A customized column-and- constraint generation approach is implemented and shown to be more effective than Benders cutting plane method. Numerical experiments are performed on real data and management insights on system design are … Read more

## An Exact Algorithm for Two-stage Robust Optimization with Mixed Integer Recourse Problems

In this paper, we consider a linear two-stage robust optimization model with a mixed integer recourse problem. Currently, this type of two-stage robust optimization model does not have any exact solution algorithm available. We first present a set of sufficient conditions under which the existence of an optimal solution is guaranteed. Then, we present a … Read more

## Solving Two-stage Robust Optimization Problems by A Constraint-and-Column Generation Method

We present a constraint-and-column generation algorithm to solve two-stage robust optimization problems. Compared with existing Benders style cutting plane methods, it is a general procedure with a unified approach to deal with optimality and feasibility. A computational study on a two-stage robust location-transportation problem shows that it performs an order of magnitude faster. Also, it … Read more