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The 15th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR) was held in Delft, The Netherlands, from June 26 to June 29, 2018. It was co-located with the International Conference on Automated Planning and Scheduling (ICAPS). The aim of the CPAIOR conference is to bring together interested researchers from Constraint Programming (CP), Artificial Intelligence (AI), and Operations Research (OR) to present new techniques or applications, and to provide an opportunity for researchers in one area to learn about techniques in the others. A main objective of this conference series is also to give these researchers the opportunity to show how the integration of techniques from different fields can lead to interesting results on large and complex problems. All papers submitted to the conference were subject to a rigorous peer-review process. Accepted papers were either formally published in the conference proceedings [1], or invited to be published exclusively through a fast-track process in the Constraints journal. Only a small selection of outstanding papers was invited to the fast track. Before being accepted into Constraints, the resulting papers were subject to another round of reviewing to ensure the high standards for this journal. This issue collects the three papers that were accepted to the fast track: The paper Deep Neural Networks and Mixed Integer Linear Optimization by Matteo Fischetti and Jason Jo studies the use of 0/1 mixed integer linear programming models for deep neural networks. The paper also analyzes a bound-tightening mechanism, and demonstrates the performance of the 0/1 MILP model on two applications in the context of feature visualization and adversarial machine learning.

Introduction to the CPAIOR 2018 fast track issue