The merging of optimization and simulation technologies has seen a remarkable growth in recent years. A Google search on “Simulation Optimization” returns more than six hundred and eighteen thousand pages where this phrase appears. The content of these pages ranges from articles, conference presentations and books to software, sponsored work and consultancy. This is an area that has sparked as much interest in the academic world as in practical settings.

A principal reason underlying the importance of simulation optimization is that many real-world problems in optimization are too complex to be given tractable mathematical formulations. Multiple nonlinearities, combinatorial relationships and uncertainties often render challenging practical problems inaccessible to modeling except by resorting to simulation – an outcome that poses grave difficulties for classical optimization methods. In such situations, recourse is commonly made to itemizing a series of scenarios in the hope that at least one will give an acceptable solution. Consequently, a long-standing goal in both the optimization and simulation communities has been to create a way to guide a series of simulations to produce high quality solutions, in the absence of tractable mathematical structures.

Applications include the goals of finding:

• the best configuration of machines for production scheduling
• the best integration of manufacturing, inventory and distribution
• the best layouts, links and capacities for network design
• the best investment portfolio for financial planning
• the best utilization of employees for workforce planning
• the best location of facilities for commercial distribution
• the best operating schedule for electrical power planning
• the best assignment of medical personnel in hospital administration
• the best setting of tolerances in manufacturing design
• the best set of treatment policies in waste management

and many other objectives.

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