A multiplier adjustment method for the generalized assignment problem
Management Science
A new algorithm for the 0-1 knapsack problem
Management Science
Computers and Operations Research
An efficient preprocessing procedure for the multidimensional 0–1 knapsack problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
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
Simulating realistic set covering problems with known optimal solutions
Computers and Industrial Engineering
Problem reduction heuristic for the 0-1 multidimensional knapsack problem
Computers and Operations Research
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
First-level tabu search approach for solving the multiple-choice multidimensional knapsack problem
International Journal of Metaheuristics
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In many computational experiments, correlation is induced between certain types of coefficients in synthetic (or simulated) instances of classical optimization problems. Typically, the correlations that are induced are only qualified---that is, described by their presumed intensity. We quantify the population correlations induced under several strategies for simulating 0--1 knapsack problem instances and also for correlation-induction approaches used to simulate instances of the generalized assignment, capital budgeting (or multidimensional knapsack), and set-covering problems. We discuss implications of these correlation-induction methods for previous and future computational experiments on simulated optimization problems.