.png){kind=icon}
Return
AN APPROXIMATION ALGORITHM FOR SOLVING UNCONSTRAINED 2-DIMENSIONAL KNAPSACK-PROBLEMS
.png){kind=icon}
PRE
AI
DOI：10.1016/0377-2217(93)E0221-I.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An efficient heuristic for solving two-dimensional knapsack problems is proposed. The algorithm selects an optimal subset of optimal generated strips by solving a sequence of one-dimensional knapsack problems. We show that the number of these knapsacks can be reduced to only four knapsacks. The algorithm gives an excellent worst-case experimental approximation ratio (0.98), and a high percentage of optimal solutions (91%). From this heuristic, we derive an approximation algorithm for which we prove some refined bounds and we show that its approximation ratio is 4/9. Our numerical study on large size instances shows the efficiency of these algorithms for solving real-world problems which are hardly handled by other known methods, which are often limited by computer storage facilities.
Keywords:
2-DIMENSIONAL KNAPSACK
2-SPACE KNAPSACK
CUTTING STOCK PROBLEMS
KNAPSACK
APPROXIMATION ALGORITHMS
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
No organization information available
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSaveShareSave
View more