Distributionally Robust Workforce Scheduling in Call Centers with Uncertain Arrival Rates

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The relationship between morphology and locomotion performance in amphibious fish remains poorly understood, particularly in axial-appendage-based and appendage-based movements. To address this, we introduce Polymander, a reconfigurable robot capable of mi ...

This paper presents a novel hybrid framework for generating and updating a synthetic population. We call it hybrid because it combines model-based and data-driven approaches. Existing generators produce a snapshot of synthetic data that becomes outdated ov ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

Vehicle sharing systems (VSSs) allow users to rent vehicles for a short period of time, in a more flexible and convenient manner compared to the traditional vehicle rental services. The long-term VSS subscription replaces the need for contract signing for ...

In the current era of big data, aggregation queries on high-dimensional datasets are frequently utilized to uncover hidden patterns, trends, and correlations critical for effective business decision-making. Data cubes facilitate such queries by employing p ...

Urban mobility services face the challenge of planning their operations efficiently while complying with user preferences. In this paper, we introduce a new mathematical model called a choice-driven dial-a-ride problem (CD-DARP) which is a generalization o ...

This paper examines the minimization of the cost for an expected random production output, given an assembly of finished goods from two random inputs, matched in two categories. We describe the optimal input portfolio, first using the standard normal appro ...

Years of globalization, outsourcing and cost cutting have increased supply chain vulnerability calling for more effective risk mitigation strategies. In our research, we analyze supply chain disruptions in a production setting. Using a bilevel optimization ...

To answer the growing demand for data computing, data storage and data transmission, the current trend is to construct centralized hyperscale data centers, whose power usage effectiveness (PUE) has substantially improved in recent years. Still, the operati ...

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

Many robotics problems are formulated as optimization problems. However, most optimization solvers in robotics are locally optimal and the performance depends a lot on the initial guess. For challenging problems, the solver will often get stuck at poor loc ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

We propose a statistically optimal approach to construct data-driven decisions for stochastic optimization problems. Fundamentally, a data-driven decision is simply a function that maps the available training data to a feasible action. It can always be exp ...

A traditional machine learning pipeline involves collecting massive amounts of data centrally on a server and training models to fit the data. However, increasing concerns about the privacy and security of user's data, combined with the sheer growth in the ...

Decentralized algorithms for stochastic optimization and learning rely on the diffusion of information through repeated local exchanges of intermediate estimates. Such structures are particularly appealing in situations where agents may be hesitant to shar ...

Distributionally Robust Workforce Scheduling in Call Centers with Uncertain Arrival Rates

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Investigating the effect of morphology on the terrestrial gaits of amphibious fish using a reconfigurable robot

Hybrid Simulator for Capturing Dynamics of Synthetic Populations

Reliable data-driven decision-making through optimal transport

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Robust optimization of control parameters for WEC arrays using stochastic methods

Data-driven Methods for Control: from Linear to Lifting

Hardware-Software co-design Methodologies for Edge AI Optimization

A Framework for Autonomic Computing for In Situ Imageomics

Demand-based operations of vehicle sharing systems

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Choice-driven dial-a-ride problem for demand responsive mobility service

Optimal Matching of Random Parts

Modeling and mitigating supply chain disruptions as a bilevel network flow problem

Decentralized ICT integration in residential buildings

A note on universal inference

Memory of Motion for Initializing Optimization in Robotics

Predicting in Uncertain Environments: Methods for Robust Machine Learning

A Pareto Dominance Principle for Data-Driven Optimization

Optimization methods for collaborative learning

Graph-Homomorphic Perturbations For Private Decentralized Learning