CP 2018 - CP and Operations Research

Constraint programming (CP) has played a pivotal role in the advancement of new modelling and solution techniques in operations research (OR). While global structure is central to inference methods in traditional OR algorithms, the emphasis on individual constraints in CP provides a more flexible modelling language which allows for the exploitation of local substructure that a global view often misses. The combination of these complementary strengths has led to the development of state-of-the-art methodologies in a variety of practical problems, including scheduling, energy and power systems, machine learning, revenue management, vehicle routing, queuing, and global optimization, to name a few.
The purpose of this track is to serve as a venue for research that explores new techniques for the integration or CP and OR, or for novel and interesting applications of CP in OR problems. Examples of possible topics include advances in decomposition methods (such as Benders decomposition and branch-and-price methods), multicriteria optimization, new ways of exploiting OR relaxations (such as linear programming and semidefinite programming) into CP, and new inference methodologies based on dynamic programming or machine learning techniques.