19 citations · 37 across the 7 of their papers we have counts for
8 papers
Learning-Augmented Optimization for Strategic Two-Echelon Spare Parts Network Design
Donato Maragno, Marco Caserta, Alberto Sinigaglia +3
We study the strategic design of a two-echelon spare-parts inventory network where evaluating each candidate topology requires an expensive inventory optimization model. The design…
Output-Constrained Decision Trees
Hüseyin Tunç, Doğanay Özese, Ş. İlker Birbil +3
Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This…
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
Segev Wasserkrug, Leonard Boussioux, Dick den Hertog +4
Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to impor…
Finding Regions of Counterfactual Explanations via Robust Optimization
Donato Maragno, Jannis Kurtz, Tabea E. Röber +3
Counterfactual explanations play an important role in detecting bias and improving the explainability of data-driven classification models. A counterfactual explanation (CE) is a m…
Counterfactual Explanations Using Optimization With Constraint Learning
Donato Maragno, Tabea E. Röber, Ilker Birbil
To increase the adoption of counterfactual explanations in practice, several criteria that these should adhere to have been put forward in the literature. We propose counterfactual…
Learn Global and Optimize Local: A Data-Driven Methodology for Last-Mile Routing
Mayukh Ghosh, Alex Kuiper, Roshan Mahes +1
In last-mile routing, the task of finding a route is often framed as a Traveling Salesman Problem to minimize travel time and associated cost. However, solutions stemming from this…
Mixed-Integer Optimization with Constraint Learning
Donato Maragno, Holly Wiberg, Dimitris Bertsimas +3
We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which c…
Optimization with Constraint Learning: A Framework and Survey
Adejuyigbe Fajemisin, Donato Maragno, Dick den Hertog
Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas. If data is however available, these data can…