Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 21st International C Bistra Dilkina Conference pro

Falter

书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research影响因子(影响力)




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research影响因子(影响力)学科排名




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research网络公开度




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research网络公开度学科排名




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引频次




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research被引频次学科排名




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research年度引用学科排名




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research读者反馈




书目名称Integration of Constraint Programming, Artificial Intelligence, and Operations Research读者反馈学科排名

疏忽

匍匐

,Acquiring Constraints for a Non-linear Transmission Maintenance Scheduling Problem,heduled to be withdrawn from the network, it becomes unavailable and can lead to power outages when other adjacent equipment fails. This problem is commonly referred to as a . (TMS) problem and remains a challenge for power utilities. Numerous combinatorial constraints must be satisfied to ensure th

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,Efficiently Mining Closed Interval Patterns with Constraint Programming,ile numerous CP models have been designed for mining on binary data, there does not exist any model designed for mining on numerical datasets. Therefore these kinds of datasets need to be pre-processed to fit the existing methods. Afterward a post-processing is also required to recover the patterns

鞭子

宿醉

,Single Constant Multiplication for SAT,SAT) solvers. This problem, known as the Single Constant Multiplication (SCM) problem, is widely studied for its application in hardware design, but its techniques are currently not employed for SAT encodings. Considering the smaller and variable bit sizes and the different cost of operations in SAT

BORE

,Towards a Generic Representation of Combinatorial Problems for Learning-Based Approaches,er or in conjunction with traditional optimization algorithms. In both scenarios, the challenge lies in encoding the targeted combinatorial problems into a structure compatible with the learning algorithm. Many existing works have proposed problem-specific representations, often in the form of a gra

有说服力

Bernstein-test

,Decision-Focused Predictions via Pessimistic Bilevel Optimization: A Computational Study,ly, and then a deterministic optimization problem is solved. However, the decisions produced by this so-called . procedure can be highly sensitive to uncertain parameters. In this work, we contribute to recent efforts in producing . predictions, i.e., to build predictive models constructed to minimi

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