Publications related to Workforce Allocation for Social Engagement Services via Stochastic Optimization

Related publications (20)

Robust Optimization for Product Line Design

In order to extract maximum surplus from a heterogeneous customer base it is common practice for firms to offer a portfolio of differentiated products, from which consumers of different preferences can select their favorite items. The standard model of pro ...

EPFL2025

A learning-based approach to stochastic optimal control under reach-avoid constraint

Maryam Kamgarpour, Tingting Ni

We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

Association for Computing Machinery2025

A Box-Spline Framework for Inverse Problems With Continuous-Domain Sparsity Constraints

Michaël Unser, Aleix Boquet I Pujadas, Mehrsa Pourya

The formulation of inverse problems in the continuum eliminates discretization errors and allows for the exact incorporation of priors. In this paper, we formulate a continuous-domain inverse problem over a search space of continuous and piecewise-linear f ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

Daniel Kuhn, Soroosh Shafieezadeh Abadeh, Bahar Taskesen

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard. Even though such problems are ubiquitous in statistics, ...

2023

An Integrated Approach to Designing Robust Turbocompressors on Gas Bearings Through Surrogate Modeling and Constrained Multi-Objective Optimization

Jürg Alexander Schiffmann, Soheyl Massoudi, Cyril Picard

Designing turbocompressors is a complex and challenging task, as it involves balancing conflicting objectives such as efficiency, stability, and robustness against manufacturing deviations. This paper proposes an integrated design methodology for turbocomp ...

ASME Amer Soc Mechanical Engineers2023

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

A New Lighting on Analytical Discrete Sensitivities in the Context of IsoGeometric Shape Optimization

Thibaut Hirschler

Isogeometric shape optimization has been now studied for over a decade. This contribution aims at compiling the key ingredients within this promising framework, with a particular attention to sensitivity analysis. Based on all the researches related to iso ...

2021

Assisted specification of discrete choice models

Michel Bierlaire, Timothy Michael Hillel, Nicola Marco Ortelli

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables ...

ELSEVIER SCI LTD2021

Turning PathFinder Upside -Down: 1-4,xploring FPGA Switch -Blocks by Negotiating Switch Presence

Paolo Ienne, Stefan Nikolic

Automated switch-block exploration gains in importance as technology scaling brings more emphasis on the physical constraints, making it insufficient to rely on abstract measures of routability alone. In this work, we take an approach that significantly di ...

IEEE COMPUTER SOC2021

Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms

Michel Bierlaire, Virginie Janine Camille Lurkin, Timothy Michael Hillel, Gael Lederrey

The emergence of Big Data has enabled new research perspectives in the discrete choice community. While the techniques to estimate Machine Learning models on a massive amount of data are well established, these have not yet been fully explored for the esti ...

2021

A Mixed-Integer Linear Programming Formulation for Optimizing Multi-Scale Material and Energy Integration

François Maréchal, Ivan Daniel Kantor, Hür Ebuzer Bütün, Jean-Loup Sylvain Robineau

This research presents a mathematical formulation for optimizing integration of complex industrial systems from the level of unit operations to processes, entire plants, and finally to considering industrial symbiosis opportunities between plants. The fram ...

2020

Joint Graph-based Depth Refinement and Normal Estimation

Pascal Frossard, Mireille El Gheche, Mattia Rossi

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings. These scenes are characterized by a prevalence of human made structures, which in most of the cases, are either ...

2020

Optimum design of low environmental impact structures through component reuse

Jan Friedrich Georg Brütting

Load-bearing systems (structures) in buildings and infrastructure contribute to a significant share of adverse environmental impacts (EI) because of resource- and energy-intensive fabrication. A currently overlooked circular economy strategy to reduce EI i ...

EPFL2020

A convex model for induction motor starting transients imbedded in an OPF-based optimization problem

Sidi-Rachid Cherkaoui, Hossein Sekhavatmanesh

Large horsepower induction motors play a critical role in the operation of industrial facilities. In this respect, the distribution network operators dedicate a high priority to the operational safety of these motor loads. In this paper, the induction moto ...

Elsevier2020

Distributed Multi-Building Coordination for Demand Response

Colin Neil Jones, Altug Bitlislioglu

This paper presents a distributed optimization algorithm tailored for solving optimal control problems arising in multi-building coordination. The buildings coordinated by a grid operator, join a demand response program to balance the voltage surge by usin ...

ELSEVIER2020

Dynamic lane restrictions on congested arterials

Nikolaos Geroliminis

Buses operating in mixed traffic experience problems with delay and travel time variability in peak periods. Numerous transit priority measures are found in the literature but most rely on separating buses from other vehicles. Complete separation such as d ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Estimation technique for offset-free economic MPC based on modifier adaptation

Dominique Bonvin

Economic model predictive control formulations that combine online optimizing control with offset-free methodologies such as modifier adaptation have been proposed recently. These new algorithms are able to achieve asymptotic optimal performance despite th ...

ELSEVIER2020

Overcapacity in European power systems: Analysis and robust optimization approach

Michel Bierlaire, François Maréchal, Frédéric Louis François Babonneau, Stefano Moret

The planning of national power systems is traditionally based on long-term forecasts of electricity demand and fuel prices. However, over such long time horizons (20-50 years), these forecasts often prove to be inaccurate. As an example, wrong projections ...

ELSEVIER SCI LTD2020

Structures that adapt to loads through large shape changes: design and optimization

Ian Smith, Gennaro Senatore, Arka Prabhata Reksowardojo

The construction industry is a major contributor to the consumption of mined material resources and to the global energy demand. It is thus important to consider energy and material efficiency criteria for the design of civil structures. Through sensing an ...

Taylor & Francis2019

Distributed Inference Over Networks Under Subspace Constraints

Ali H. Sayed, Stefan Vlaski, Roula Nassif

This paper considers optimization problems over networks where agents have individual objectives to meet, or individual parameter vectors to estimate, subject to subspace constraints that enforce the objectives across the network to lie in a low-dimensiona ...

IEEE2019