Pareto suboptimal solutions to large-scale multiobjective multidimensional knapsack problems with assessments of Pareto suboptimality gaps

Abstract

When solving large-scale multiobjective optimization problems, solvers can get stuck with the memory or time limit. In such cases, one is left with no information how far is the best feasible solution, found before the optimization process has stopped, to the true Pareto optimal solution. In this work, we show how to provide such information when solving multiobjective multidimensional knapsack problems by a commercial mixed-integer linear solver. We illustrate the proposed approach on biobjective multidimensional knapsack problems derived from singleobjective multidimensional knapsack problems from the Beasley OR Library.