Presolving and cutting planes for the generalized maximal covering location problem

This paper considers the generalized maximal covering location problem (GMCLP) which establishes a fixed number of facilities to maximize the weighted sum of the covered customers, allowing customer weights to be positive or negative. Due to the huge number of linear constraints to model the covering relations between the candidate facility locations and customers, and particularly the poor linear programming (LP) relaxation, the GMCLP is extremely difficult to solve by state-of-the-art mixed integer programming (MIP) solvers. To improve the computational performance of MIP-based approaches for solving GMCLPs, we propose customized presolving and cutting plane techniques, which are isomorphic aggregation, dominance reduction, and two-customer inequalities. The isomorphic aggregation and dominance reduction can not only reduce the problem size but also strengthen the LP relaxation of the MIP formulation of the GMCLP. The two-customer inequalities can be embedded into a branch-and-cut framework to further strengthen the LP relaxation of the MIP formulation on the fly. By extensive computational experiments, we show that all three proposed techniques can substantially improve the capability of MIP solvers in solving GMCLPs. In particular, for a testbed of 40 instances with identical numbers of customers and candidate facility locations in the literature, the proposed techniques enable us to provide optimal solutions for 13 previously unsolved benchmark instances; for a testbed of 336 instances where the number of customers is much larger than the number of candidate facility locations, the proposed techniques can turn most of them from intractable to easily solvable.