Int. J. of Industrial and Systems Engineering   »   2008 Vol.3, No.5

 

 

Title: Exploiting empirical knowledge for bi-dimensional knapsack problem heuristics

 

Author: Yong Kun Cho, James T. Moore, Raymond R. Hill, Charles H. Reilly

 

Addresses:
Department of Operational Science, US Air Force Institute of Technology, AFIT/ENS, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA.
Department of Operational Science, US Air Force Institute of Technology, AFIT/ENS, 2950 Hobson Way, Wright-Patterson AFB, OH 45433, USA.
Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, 207 Russ Engineering Center, 3640 Col Glenn Highway, Dayton, OH 45424, USA.
Department of Industrial Engineering and Management Systems, University of Central Florida, P.O. Box 162993, Orlando, FL 32816-2993, USA

 

Abstract: The Multidimensional Knapsack Problem (MKP) has been used to model a variety of practical applications. Due to its combinatorial nature, heuristics are often employed to quickly find good solutions to MKPs. There have been a variety of heuristics proposed for MKP and a plethora of empirical studies comparing the performance of these heuristics. However, little has been done to garner a deeper understanding of why certain heuristics perform well on certain types of problems and others do not. Using a broad range of practical MKP test problems, we use three representative heuristics and conduct an empirical study aimed at gaining a deeper understanding of heuristic procedure performance as a function of test problem constraint characteristics. Our focus is on the Two-dimensional Knapsack Problem (2KP). New insights developed regarding greedy heuristic performance are exploited to yield two new heuristics whose performance is more robust than that of three legacy heuristics on our test problem set and on benchmark sets of MKP problems. A competitive test of these new heuristics against a set of legacy heuristics, using both existing test problem sets and a new systematically developed test problem set demonstrate the superior, robust performance of our new heuristics.

 

Keywords: heuristic optimisation; algorithms design; test problem generation; two-dimensional knapsack problem; 2KP; multidimensional knapsack problem; MKP; greedy heuristics.

 

DOI: 10.1504/IJISE.2008.018231

 

Int. J. of Industrial and Systems Engineering, 2008 Vol.3, No.5, pp.530 - 548

 

Available online: 13 May 2008

 

 

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