Algorithmic improvements on dynamic programming for the bi-objective {0, 1} knapsack problem

This paper presents several methodological and algorithmic improvements over a state-ofthe-art dynamic programming algorithm for solving the bi-objective {0, 1} knapsack problem. The variants proposed make use of new definitions of lower and upper bounds, which allow a large number of states to be discarded. The computation of these bounds are based on the application of dichotomic search, definition of new bound sets, and bi-objective simplex algorithms to solve the relaxed problem. Although these new techniques are not of a common application for dynamic programming, we show that the best variants tested in this work can lead to an average improvement of 20% in CPU-time and significant less memory usage than the original approach in a wide benchmark set of instances, even for the most difficult ones in the literature.