Logic-Based Benders Decomposition: Theory and Applications

This book is the first comprehensive guide to logic-based  Benders decomposition (LBBD), a general and versatile method for  breaking large, complex optimization problems into components that are  small enough for practical solution. The author introduces logic-based  Benders decomposition for optimization, which substantially generalizes  the classical Benders method. It can reduce solution times by orders of  magnitude and allows decomposition to be applied to a much wider variety  of optimization problems. On the theoretical side, this book provides a  full account of inference duality concepts that underlie LBBD, as well  as a description of how LBBD can be combined with stochastic and robust  optimization, heuristic methods, and decision diagrams. It also  clarifies the connection between LBBD and combinatorial Benders cuts for  mixed integer programming. On the practical side, it explains how LBBD  has been applied to a rapidly growing variety of problem domains. After  describing basic theory, this book provides a comprehensive review of  the rapidly growing literature that describes these applications, in  each case explaining how LBBD is adapted to the problem at hand. In  doing so this work provides a sourcebook of ideas for applying LBBD to  new problems as they arise.