The colored knapsack problem: structural properties and exact algorithms

TL;DR

This paper introduces the Colored Knapsack Problem, analyzes its properties, proposes exact algorithms, and demonstrates their superior performance over existing solvers through extensive computational experiments.

Contribution

The paper presents the first exact dynamic programming algorithms for CKP and analyzes its linear programming relaxation, providing new theoretical insights and practical solution methods.

Findings

CKP is weakly NP-hard.

Proposed algorithms outperform MIP solvers on benchmark instances.

Optimal solutions have at most two fractional items in LP relaxation.

Abstract

We introduce and study a novel generalization of the classical Knapsack Problem (KP), called the Colored Knapsack Problem (CKP). In this problem, the items are partitioned into classes of colors and the packed items need to be ordered such that no consecutive items are of the same color. We establish that the problem is weakly NP-hard and propose two exact dynamic programming algorithms with time complexities of $\mathcal{O}(bn^4)$ and $\mathcal{O}(b^2n^3)$, respectively. To enhance practical performance, we derive various dominance and fathoming rules for both approaches. From a theoretical perspective, we analyze the linear programming relaxation of the natural CKP formulation, proving that an optimal solution exists with at most two fractional items. We also show that the relaxation can be solved in $\mathcal{O}(n)$ time, matching the complexity of the classical KP. Finally, we establish a comprehensive benchmark of CKP instances, derived from the Colored Bin Packing Problem. Extensive computational experiments demonstrate that the proposed dynamic programming algorithms significantly outperform state-of-the-art MIP solvers on most of these instances.