A tighter cut generation strategy for acceleration of Benders decomposition
This paper presents a novel strategy for speeding up the classical Benders decomposition for large-scale mixed integer linear programming problems. The proposed method is particularly useful when the optimality cut is difficult to obtain. A ratio of distances from a feasible point to an infeasible point and a feasibility cut is used as a metric to determine the tightest constraint for the region located by the feasible point, thus improving the convergence rate. Application of the proposed approach to a multi-product batch plant scheduling problem shows substantial improvement both in the computational time and the number of iterations. (C) 2012 Elsevier Ltd. All rights reserved.
mixed integer linear programming feasibility cut scheduling dinkelbach's method fractional programming cplex