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Found 9 papers, 6 papers with code
Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs
Given any duals for these constraints, called Lagrangian Multipliers (LMs), it returns a bound on the optimal value of the MILP, and Lagrangian methods seek the LMs giving the best such bound.
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows
With the rise of e-commerce and increasing customer requirements, logistics service providers face a new complexity in their daily planning, mainly due to efficiently handling same day deliveries.
Learning-based Online Optimization for Autonomous Mobility-on-Demand Fleet Control
Autonomous mobility-on-demand systems are a viable alternative to mitigate many transportation-related externalities in cities, such as rising vehicle volumes in urban areas and transportation-related pollution.
Explainable Data-Driven Optimization: From Context to Decision and Back Again
Data-driven optimization uses contextual information and machine learning algorithms to find solutions to decision problems with uncertain parameters.
Learning with Combinatorial Optimization Layers: a Probabilistic Approach
We demonstrate its abilities using a pathfinding problem on video game maps as guiding example, as well as three other applications from operations research.
Don't Explain Noise: Robust Counterfactuals for Randomized Ensembles
Counterfactual explanations describe how to modify a feature vector in order to flip the outcome of a trained classifier.
Optimal Counterfactual Explanations in Tree Ensembles
Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions.
Learning to solve the single machine scheduling problem with release times and sum of completion times
In this paper, we focus on the solution of a hard single machine scheduling problem by new heuristic algorithms embedding techniques from machine learning field and scheduling theory.
Integer programming for weakly coupled stochastic dynamic programs with partial information
The second is a collection of mathematical programming formulations and algorithms which provide tractable policies and upper bounds for weakly coupled POMDPs.
Optimization and Control
