Heuristic and exact fixation-based approaches for the discounted 0-1 knapsack problem

TL;DR

This paper introduces a two-phase approach combining fixation rules and dynamic programming to efficiently solve the discounted 0-1 knapsack problem, significantly reducing computation time on benchmark instances.

Contribution

It proposes a novel fixation-based preprocessing method for the discounted 0-1 knapsack problem, enhancing solution efficiency over existing techniques.

Findings

Fixation techniques significantly reduce problem size.

Preprocessing accelerates dynamic programming solutions.

Approach effectively solves benchmark instances.

Abstract

In this paper we consider the discounted 0-1 knapsack problem (DKP), which is an extension of the classical knapsack problem where a set of items is decomposed into groups of three items. At most one item can be chosen from each group and the aim is to maximize the total profit of the selected items while respecting the knapsack capacity constraint. The DKP is a relatively recent problem in the literature. In this paper we propose a two-phase approach in which the problem is reduced by applying exact and / or heuristic fixation rules in a first phase that can be viewed as a preprocessing phase. The remaining problem can then be solved by dynamic programming. Experiments performed on available instances in the literature show that the fixation techniques are very useful to solve these instances. Indeed, the preprocessing phase greatly reduces the size of these instances, leading to a significant reduction in the time required for dynamic programming to provide an optimal solution.