Chance-Constrained Optimization under Limited Distributional Information: A Review of Reformulations Based on Sampling and Distributional Robustness

Chance-constrained programming (CCP) is one of the most difficult classes of optimization problems that has attracted the attention of researchers since the 1950s. In this survey, we focus on cases when only a limited information on the distribution is available, such as a sample from the distribution, or the moments of the distribution. We first … Read more

Scheduling the Brazilian OR Conference

In this paper, we show how to efficiently schedule the Brazilian OR conference using a matheuristic approach. The event has traditionally around 300 presentations across a period of 3 to 4 days, and building a schedule for the technical sessions is an arduous task. The proposed algorithm integrates the concepts of iterated local search and … Read more

A Survey on Mixed-Integer Programming Techniques in Bilevel Optimization

Bilevel optimization is a field of mathematical programming in which some variables are constrained to be the solution of another optimization problem. As a consequence, bilevel optimization is able to model hierarchical decision processes. This is appealing for modeling real-world problems, but it also makes the resulting optimization models hard to solve in theory and … Read more

Two decades of blackbox optimization applications

This work reviews blackbox optimization applications over the last twenty years, addressed using direct search optimization methods. Emphasis is placed on the Mesh Adaptive Direct Search (MADS) derivative-free optimization algorithm. The core of the document describes applications in three specific fields: Energy, materials science, and computational engineering design. Other applications in science and engineering as … Read more

Conference scheduling: a clustering-based approach

Scheduling the technical sessions of scientific events is an arduous task commonly faced by many organizers worldwide. Due the particularities of each conference, there is no consensus regarding the problem definition, and researchers have tackled each specific case individually. Despite their distinct characteristics, one often expects the sessions to be composed of presentations of similar … Read more

An annotated bibliography of GRASP, Part II: Applications

A greedy randomized adaptive search procedure (GRASP) is a metaheuristic for combinatorial optimization. It is a multi-start or iterative process, in which each GRASP iteration consists of two phases, a construction phase, in which a feasible solution is produced, and a local search phase, in which a local optimum in the neighborhood of the constructed … Read more