.png){kind=icon}
.png){kind=icon}
Return
An effective dynamic programming algorithm for the minimum-cost maximal knapsack packing problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ejor.2017.03.061.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Given a set of items with profits and weights and a knapsack capacity, we study the problem of finding a maximal knapsack packing that minimizes the profit of the selected items. We propose an effective dynamic programming (DP) algorithm which has a pseudo-polynomial time complexity. We demonstrate the equivalence between this problem and the problem of finding a minimal knapsack cover that maximizes the profit of the selected items. In an extensive computational study on a large and diverse set of benchmark instances, we demonstrate that the new DP algorithm outperforms a state-of-the-art commercial mixed-integer programming (MIP) solver applied to the two best performing MIP models from the literature. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Combinatorial optimization
Maximal knapsack packing
Minimal knapsack cover
Dynamic programming
Integer programming
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
2.2W
Citations:
6.4W
Organization
View more organizations