Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 20th International C Andre A. Cire Conference proc

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,Online Learning for Scheduling MIP Heuristics, attributed to heuristics. Since their behavior is highly instance-dependent, relying on hard-coded rules derived from empirical testing on a large heterogeneous corpora of benchmark instances might lead to sub-optimal performance. In this work, we propose an online learning approach that adapts the

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,Contextual Robust Optimisation with Uncertainty Quantification,ity of optimisation to parameter estimation. This is achieved by integrating uncertainty quantification (UQ) methods for supervised learning into the ambiguity sets for distributionally robust optimisation (DRO). The pipelines leverage learning to produce contextual/conditional ambiguity sets from s

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,ZDD-Based Algorithmic Framework for Solving Shortest Reconfiguration Problems,ams (ZDDs), a data structure for representing families of sets. In general, a reconfiguration problem checks if there is a step-by-step transformation between two given feasible solutions (e.g., independent sets of an input graph) of a fixed search problem, such that all intermediate results are als

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,Neural Networks for Local Search and Crossover in Vehicle Routing: A Possible Overkill?,achine learning algorithms. In this study, we investigate the use of predictions from graph neural networks (GNNs) in the form of heatmaps to improve the Hybrid Genetic Search (HGS), a state-of-the-art algorithm for the Capacitated Vehicle Routing Problem (CVRP). The crossover and local-search compo

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,Scalable and Near-Optimal ,-Tube Clusterwise Regression, distinct regression trends. Due to the inherent difficulty in simultaneously optimizing clustering and regression objectives, it is not surprising that existing optimal CLR approaches do not scale beyond 100 s of data points. In an effort to provide more scalable and optimal CLR methods, we propose