Constraint programming is a powerful paradigm for solving combinatorial search problems
that draws on a wide range of techniques from artificial intelligence, computer science …

Benders decomposition uses a strategy of``learning from one's mistakes.''The aim of this
paper is to extend this strategy to a much larger class of problems. The key is to generalize …

Mixed-integer programs (MIPs) involving logical implications modeled through big-M
coefficients are notoriously among the hardest to solve. In this paper, we propose and …

We combine mixed-integer linear programming (MILP) and constraint programming (CP) to
solve an important class of planning and scheduling problems. Tasks are allocated to …

Pre-runtime scheduling of large-scale electronic systems, as those in modern aircraft, can be
computationally challenging. In this paper, we study a distributed integrated modular avionic …

We combine mixed integer linear programming (MILP) and constraint programming (CP) to
solve planning and scheduling problems. Tasks are allocated to facilities using MILP and …

Classical scheduling formulations typically assume static resource requirements and focus
on deciding when to start the problem activities, so as to optimize some performance metric …

Logic-based Benders decomposition (LBBD) is a substantial generalization of classical
Benders decomposition that, in principle, allows the subproblem to be any optimization …

We present a variety of approaches for solving the post enrolment-based course timetabling
problem, which was proposed as Track 2 of the 2007 International Timetabling Competition …

Publisher Summary This chapter reviews that number of operations research (OR) methods
have found their way into constraint programming (CP). This development is entirely natural …