Compressed data structures for bi-objective {0,1}-knapsack problems

Solving multi-objective combinatorial optimization problems to optimality is a computationally expensive task. The development of implicit enumeration approaches that efficiently explore certain properties of these problems has been the main focus of recent research. This article proposes algorithmic techniques that extend and empirically improve the memory usage of a dynamic programming algorithm for computing the set of efficient solutions both in the objective space and in the decision space for the bi-objective knapsack problem. An in-depth experimental analysis provides further information about the performance of these techniques with respect to the trade-off between CPU time and memory usage.