Proceedings of the 33nd conference on Winter simulation
Experimental Evaluation of Heuristic Optimization Algorithms: A Tutorial
Journal of Heuristics
Behavior of the NORTA method for correlated random vector generation as the dimension increases
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Hard Problem Generation for MKP
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Should we model dependence and nonstationarity, and if so how?
WSC '05 Proceedings of the 37th conference on Winter simulation
MS'06 Proceedings of the 17th IASTED international conference on Modelling and simulation
Synthetic Optimization Problem Generation: Show Us the Correlations!
INFORMS Journal on Computing
Problem reduction heuristic for the 0-1 multidimensional knapsack problem
Computers and Operations Research
Shift-and-merge technique for the DP solution of the time-constrained backpacker problem
Computers and Operations Research
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
Generalising algorithm performance in instance space: a timetabling case study
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
First-level tabu search approach for solving the multiple-choice multidimensional knapsack problem
International Journal of Metaheuristics
Towards objective measures of algorithm performance across instance space
Computers and Operations Research
Hi-index | 0.01 |
This paper presents the results of an empirical study of the effects of coefficient correlation structure and constraint slackness settings on the performance of solution procedures on synthetic two-dimensional knapsack problems (2KP). The population correlation structure among 2KP coefficients, the level of constraint slackness, and the type of correlation (product moment or rank) are varied in this study. Representative branch-and-bound and heuristic solution procedures are used to investigate the influence of these problem parameters on solution procedure performance. Population correlation structure, and in particular the interconstraint component of the correlation structure, is found to be a significant factor influencing the performance of both the algorithm and the heuristic. In addition, the interaction between constraint slackness and population correlation structure is found to influence solution procedure performance.