Titlebook: Integration of Constraint Programming, Artificial Intelligence, and Operations Research; 18th International C Peter J. Stuckey Conference p

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




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




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




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

胎儿

A Computational Study of Constraint Programming Approaches for Resource-Constrained Project Schedulduling problems where the duration of activities can be reduced when scheduled after certain other activities that allow for learning relevant skills. Since per-period availabilities of renewable resources are limited and precedence requirements have to be respected, the resulting optimization probl

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Strengthening of Feasibility Cuts in Logic-Based Benders Decomposition,ack information. Therefore, an important acceleration technique in LBBD is to strengthen feasibility cuts by reducing their sizes. This is typically done by solving additional subproblems to evaluate potential cuts. In this paper, we study three cut-strengthening algorithms that differ in the comput

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A,-Based Compilation of Relaxed Decision Diagrams for the Longest Common Subsequence Problem,relies on the compilation of a relaxed multi-valued decision diagram (MDD) in a special way that is based on the principles of A. search. An extensive experimental evaluation on several standard LCS benchmark instance sets shows that the novel construction algorithm clearly outperforms a traditional

Stagger

Partitioning Students into Cohorts During COVID-19,s. One of the most effective measures to reduce virus transmission is partitioning students into discrete cohorts..In primary and middle schools, it is easy to create these cohorts (also known as “learning groups”), since students in each grade take the same set of required courses. However, in high

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Heavy-Tails and Randomized Restarting Beam Search in Goal-Oriented Neural Sequence Decoding,oblems. In these scenarios, the beam search algorithm is typically used to produce a set of high-likelihood candidate sequences that are evaluated to determine if they satisfy the goal criteria. If none of the candidates satisfy the criteria, the beam search can be restarted with a larger beam size

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