An improved linearization technique for a class of quadratic 0‐1 programming problems

The recent research on linearization techniques for solving 0‐1 quadratic programming problems focuses on providing concise models and tightening constraint bounds. In this paper, we propose a computational enhancement for a linearization technique to make the linearized model much faster to solve. We investigate the computational performance of the proposed approach, by comparing it with other linearization techniques on a class of 0‐1 quadratic programming problems. We can further speed up the proposed technique by heuristically tightening the constraint bounds, as demonstrated by solving the uncapacitated single allocation p‐hub median problem using the Civil Aeronautics Board data.