Simulated annealing: theory and applications
Simulated annealing: theory and applications
Genetic algorithms: foundations and applications
Annals of Operations Research
A review of simulation optimization techniques
Proceedings of the 30th conference on Winter simulation
Optimization in simulation: a survey of recent results
WSC '87 Proceedings of the 19th conference on Winter simulation
Simulation optimization: a survey of simulation optimization techniques and procedures
Proceedings of the 32nd conference on Winter simulation
Ranking and selection for steady-state simulation
Proceedings of the 32nd conference on Winter simulation
Tabu Search
Proceedings of the 33nd conference on Winter simulation
Simulation optimization: towards a framework for black-box simulation optimization
Proceedings of the 33nd conference on Winter simulation
Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Solving simulation optimization problems on grid computing systems
Parallel Computing - Optimization on grids - Optimization for grids
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This paper addresses the problem of optimizing a function over a finite or countable infinite set of alternatives, whenever the objective function cannot be evaluated exactly, but has to be estimated via simulation. We present an iterative method, based on the simulated annealing framework, for solving such discrete stochastic optimization problems. In the proposed method, we combine the robustness of this metaheuristic method with a statistical procedure for comparing the solutions that are generated. The focus of our work is on devising an effective procedure rather than addressing theoretical issues. In fact, in our opinion, although significant progresses have been made in studying the convergence of a number of simulation---optimization algorithms, at present there is no procedure able to consistently provide good results in a reasonable amount of time. In addition, we present a parallelization strategy for allocating simulation runs on computing resources.