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Some recent works showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) ...

Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be ...

We study the problem of coordination control of multiple traffic signals to mitigate traffic congestion. The parameters we optimize are the coordination pattern and offsets. A coordination pattern indicates which traffic signals are coordinated. Offsets ...

This paper aims to propose a novel deep learning-integrated framework for deriving reliable simulation input models through incorporating multi-source information. The framework sources and extracts multi-source data generated from construction ...

Earth moving operations are a critical component of construction and mining industries with a lot of potential for optimization and improved productivity. In this paper we combine discrete event simulation with reinforcement learning (RL) and neural ...

This paper discusses the development of an efficient algorithm that minimizes overproduction in the allocation of wafers to customer orders prior to assembly at a semiconductor production facility. This study is motivated by and tested at Nexperia's ...

This contribution introduces an innovative holistic multi-objective simheuristic approach for advanced production planning on rolling horizon basis for an European industrial food manufacturer. The optimization combines an efficient heuristic mixed-...

Emerging data that track the dynamics of large populations bring new potential for understanding human decision-making in a complex world and supporting better decision-making through the integration of continued partial observations about dynamics. ...

Coherent risk measures have received increasing attention in recent years among both researchers and practitioners. The problem of estimating a coherent risk measure can be cast as estimating the maximum expected loss taken under a set of probability ...

The generative adversarial network (GAN) had been successfully applied in many domains in the past, the GAN network provides a new approach for solving computer vision, object detection and classification problems by learning, mimicking and generating ...

As AI advances and becomes more complicated, it becomes necessary to study the safety implications of its behavior. This paper expands upon prior AI-safety research to create a model to study the harmful outcomes of multi-agent systems. In this paper, ...