Utility-based shortfall risk measure (SR) effectively captures decision maker’s risk attitude on tail losses by an increasing convex loss function. In this ...

Deep learning has redefined modern standards and performance in several areas such as computer vision and natural language processing. With increasing amou...

In time-to-event data analysis, the main object of interest is the time elapsed between the occurrence of two ordered events, say \(E_1, E_2\). Sampling fr...

Traditionally, the design of supply chains for humanitarian operations has been developed distinctly for the different disaster management phases, with littl...

Conditional Value at Risk (CVaR) is widely used to account for the preferences of a risk-averse agent in the extreme loss scenarios. To study the effectiven...

It is well known that the integrated optimization of multiple and inter-related decisions in a supply chain can bring important benefits to companies. In thi...

Mineral value chains or mining complexes involve mining, processing, stockpiling, waste management, and transportation activities. An integrated stochastic...

A new mathematical model for stochastic short-term optimization of mining complexes is presented that simultaneously optimizes the short-term extraction sequ...

This paper presents the application of adaptive simultaneous stochastic optimization with a representative branching framework to generate the strategic pl...

In derivative-free and blackbox optimization, the objective function is often evaluated through the execution of a computer program seen as a blackbox. It ...

Very large networks linking dynamical agents are now ubiquitous and the need to analyse, design and control them is evident. The emergence of the graphon the...

Graphon-based control has recently been proposed and developed to solve control problems for dynamical systems on networks which are very large or growing w...

In this paper we study the linear quadratic regulation (LQR) problem for dynamical systems coupled over large-scale networks and obtain locally computable l...

This work introduces StoMADS, a stochastic variant of the mesh adaptive direct-search (MADS) algorithm originally developed for deterministic blackbox optim...

Dealing with demand uncertainty in multi-item lot sizing problems poses huge challenges due to the inherent complexity. The resulting stochastic formulation...

This paper explores the idea that two-stage worst-case regret minimization problems with either objective or right-hand side uncertainty can be reformulated ...

The deepening penetration of renewable power generation is challenging how the minute balancing of supply and demand is carried out by power system operators...

We consider a class of min-max robust problems in which the functions that need to be robustified can be decomposed as the sum of arbitrary functions. This...

Drawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfoli...

This paper considers a dynamic Emergency Medical Services (EMS) network design problem and introduces two novel two-stage stochastic programming formulatio...