Benders decomposition with adaptive oracles for large scale optimization

Benders Decomposition for Large-Scale Uncapacitated Hub Location

This paper describes an exact algorithm capable of solving large-scale instances of an important hub location problem called the Uncapacitated Hub Location Problem with Multiple Assignments. The algorithm applies Benders decomposition to a strong path-based formulation of the problem. The standard decomposition algorithm is enhanced through the inclusion of several features such as the use of a...

Redesigning Benders Decomposition for Large-Scale Facility Location

The Uncapacitated Facility Location (UFL) problem is one of the most famous and most studied problems in the Operations Research literature. Given a set of potential facility locations, and a set of customers, the goal is to find a subset of facility locations to open, and to allocate each customer to open facilities, so that the facility opening plus customer allocation costs are minimized. In...

A limited memory adaptive trust-region approach for large-scale unconstrained optimization

This study concerns with a trust-region-based method for solving unconstrained optimization problems. The approach takes the advantages of the compact limited memory BFGS updating formula together with an appropriate adaptive radius strategy. In our approach, the adaptive technique leads us to decrease the number of subproblems solving, while utilizing the structure of limited memory quasi-Newt...

Benders’ Decomposition Methods for Structured Optimization, including Stochastic Optimization

Supply Chain Optimization by Benders Decomposition

This paper introduces a formulation and a solution methodology for the problem of optimizing the supply chain (or logistics network) of a manufacturing firm. The problem is modeled as a linear mixed-integer program with binary variables associated with design decisions such as location, product range assignment and transportation mode selection, and continuous variables representing the flows o...