The branch-and-bound (B&B) algorithmic framework has been used successfully to find
exact solutions for a wide array of optimization problems. B&B uses a tree search strategy to …

Solving optimization problems with parallel algorithms has a long tradition in OR. Its future
relevance for solving hard optimization problems in many fields, including finance, logistics …

Robust optimization (RO) is a tractable method to address uncertainty in optimization
problems where uncertain parameters are modeled as belonging to uncertainty sets that are …

The SCIP Optimization Suite is a powerful collection of optimization software that consists of
the branch-cut-and-price framework and mixed-integer programming solver SCIP, the linear …

In the literature for mixed integer programming, heuristic algorithms (particularly primal
heuristics) are often considered as stand-alone procedures; in that context, heuristics are …

In this chapter, we provide an overview of the current state of the art with respect to solution
of mixed integer linear optimization problems (MILPs) in parallel. Sequential algorithms for …

Effective computational methods are important for practitioners and researchers working in
strategic underground mine planning. We consider a class of problems that can be modeled …

Since graph neural networks (GNNs) are often vulnerable to attack, we need to know when
we can trust them. We develop a computationally effective approach towards providing …

Mixed-integer linear programming (MILP) has become a cornerstone of operations research.
This is driven by the enhanced efficiency of modern solvers, which can today find globally …

We discuss the variability in the performance of multiple runs of branch-and-cut mixed
integer linear programming solvers, and we concentrate on the one deriving from the use of …