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Found 26 papers, 16 papers with code
Trained Random Forests Completely Reveal your Dataset
Even with bootstrap aggregation, the majority of the data can also be reconstructed.
Contextual Stochastic Vehicle Routing with Time Windows
We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before making routing decisions.
A Survey of Contextual Optimization Methods for Decision Making under Uncertainty
Recently there has been a surge of interest in operations research (OR) and the machine learning (ML) community in combining prediction algorithms and optimization techniques to solve decision-making problems in the face of uncertainty.
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.
Regularization and Optimization in Model-Based Clustering
We develop more effective optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting.
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.
Bilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem
The lower-level problem learns the optimal coefficients of the decision function on a training set, using only the features selected by the upper-level.
Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?
Extensive research has been conducted, over recent years, on various ways of enhancing heuristic search for combinatorial optimization problems with machine learning algorithms.
Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts
The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function.
Optimal Decision Diagrams for Classification
Decision diagrams for classification have some notable advantages over decision trees, as their internal connections can be determined at training time and their width is not bound to grow exponentially with their depth.
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.
Semi-Supervised Clustering with Inaccurate Pairwise Annotations
Pairwise relational information is a useful way of providing partial supervision in domains where class labels are difficult to acquire.
Community Detection in the Stochastic Block Model by Mixed Integer Programming
The standard approach of community detection based on the DCSBM is to search for the model parameters that are the most likely to have produced the observed network data through maximum likelihood estimation (MLE).
Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood
The vehicle routing problem is one of the most studied combinatorial optimization topics, due to its practical importance and methodological interest.
Assortative-Constrained Stochastic Block Models
Stochastic block models (SBMs) are often used to find assortative community structures in networks, such that the probability of connections within communities is higher than in between communities.
Born-Again Tree Ensembles
The use of machine learning algorithms in finance, medicine, and criminal justice can deeply impact human lives.
PILS: Exploring high-order neighborhoods by pattern mining and injection
During the local search, each pattern is used to define one move in which 1) incompatible edges are disconnected, 2) the edges defined by the pattern are reconnected, and 3) the remaining solution fragments are optimally reconnected.
A simple and effective hybrid genetic search for the job sequencing and tool switching problem
The job sequencing and tool switching problem (SSP) has been extensively studied in the field of operations research, due to its practical relevance and methodological interest.
A concise guide to existing and emerging vehicle routing problem variants
The diversity of applications has motivated the study of a myriad of problem variants with different attributes.
An Efficient Matheuristic for the Minimum-Weight Dominating Set Problem
A minimum dominating set in a graph is a minimum set of vertices such that every vertex of the graph either belongs to it, or is adjacent to one vertex of this set.
HG-means: A scalable hybrid genetic algorithm for minimum sum-of-squares clustering
This may be related to differences of computational effort, or to the assumption that a near-optimal solution of the MSSC has only a marginal impact on clustering validity.
Heuristics for vehicle routing problems: Sequence or set optimization?
We investigate a structural decomposition for the capacitated vehicle routing problem (CVRP) based on vehicle-to-customer "assignment" and visits "sequencing" decision variables.
Large Neighborhood-Based Metaheuristic and Branch-and-Price for the Pickup and Delivery Problem with Split Loads
The core of the metaheuristic consists of a new large neighborhood search, which reduces the problem of finding the best insertion combination of a pickup and delivery pair into a route (with possible splits) to a resource-constrained shortest path and knapsack problem.
Technical Note: Split Algorithm in O(n) for the Capacitated Vehicle Routing Problem
In the vehicle routing literature, Split is usually assimilated to the search for a shortest path in a directed acyclic graph $\mathcal{G}$ and solved in $O(nB)$ using Bellman's algorithm, where $n$ is the number of delivery points and $B$ is the average number of feasible routes that start with a given customer in the giant tour.
Data Structures and Algorithms
Hybrid Metaheuristics for the Clustered Vehicle Routing Problem
The second algorithm is a Hybrid Genetic Search, for which the shortest Hamiltonian path between each pair of vertices within each cluster should be precomputed.
