MOOC
Optimization: principles and algorithms - Linear optimization
Publications associées (1 000)
Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems
In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...
EPFL2024A ride time-oriented scheduling algorithm for dial-a-ride problems
Nikolaos Geroliminis, Claudia Bongiovanni, Mor Kaspi
This paper offers a new algorithm to efficiently optimize scheduling decisions for dial-a-ride problems (DARPs), including problem variants considering electric and autonomous vehicles (e-ADARPs). The scheduling heuristic, based on linear programming theor ...
Pergamon-Elsevier Science Ltd202450 years of behavioral models for transportation and logistics
Michel Bierlaire, Léa Massé Ricard
Fifty years ago, transportation and logistics problems were primarily analyzed either from a supply-side or a demand-side perspective, with the fields of operations research and demand modeling evolving separately. Since then, there has been a growing inte ...
2024A Combination Technique for Optimal Control Problems Constrained by Random PDEs
We present a combination technique based on mixed differences of both spatial approximations and quadrature formulae for the stochastic variables to solve efficiently a class of optimal control problems (OCPs) constrained by random partial differential equ ...
2024Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning
Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...
EPFL2024Scalable constrained optimization
Modern optimization is tasked with handling applications of increasingly large scale, chiefly due to the massive amounts of widely available data and the ever-growing reach of Machine Learning. Consequently, this area of research is under steady pressure t ...
EPFL2024An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance
Nikolaos Geroliminis, Can Chen
Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...
Informs2024The Two Times Problem: Where Is the Problem?
Gruber et al. (2022) offered a framework how to explain "Physical time within human time", solving the 'two times problem: Here, I am asking whether such a problem exists at all. To question the question, I will appeal to neurobiological, evolutionary, and ...
Brill2024Spatial Modeling for Building Design Evaluation: from Visual Landscape Quality Assessment to Devaluation Risk Estimation
Zoning reform is a crucial tool for cities to adapt to contemporary challenges. However, its implementation remains challenging. Property owners, with a vested interest in the value of their neighborhoods, are sensitive to local developments and the potent ...
EPFL2024OASIS: An integrated optimisation framework for activity scheduling
Activity-based models offer the potential for a far deeper understanding of daily mobility behaviour than trip-based models. Based on the fundamental assumption that travel demand is derived from the need to do activities, they are flexible tools that aim ...
EPFL2024