Scheduling ocean transportation of crude oil
Management Science
Optimal selection of ingot sizes via set covering
Operations Research
A new algorithm for the 0-1 knapsack problem
Management Science
A comparison of alternative input models for synthetic optimization problems
WSC '93 Proceedings of the 25th conference on Winter simulation
Optimization test problems with uniformly distributed coefficients
WSC '91 Proceedings of the 23rd conference on Winter simulation
Input models for synthetic optimization problems
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
WSC' 90 Proceedings of the 22nd conference on Winter simulation
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
Synthetic Optimization Problem Generation: Show Us the Correlations!
INFORMS Journal on Computing
Experiments with parallel branch-and-bound algorithms for the set covering problem
Operations Research Letters
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This paper outlines a methodology to generate random Set Covering Problem (SCP) instances with known optimal solutions and correlated coefficients. Positive correlation between the objective function coefficients and the column sums of the SCP constraint matrix is known to affect the performance of SCP solution methods. Generating large SCP instances with known optimal solutions and realistic coefficient correlation provides a plethora of test problems with controllable problem characteristics, including correlation, and an ample opportunity to test the performance of SCP heuristics and algorithms without having to solve the problems to optimality. We describe the procedure for generating SCP instances and present the results of a computational demonstration conducted on SCP instances generated by our procedure. This computational demonstration shows that the heuristics' relative errors increase as the correlation increases, that the likelihood of finding a non-optimal solution also increases with the level of correlation, and that the quality of the solutions found is affected by the number of constraints.