Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Textbooks sometimes describe building models, running experiments, analyzing outputs, and implementing results as distinct activities in a simulation project.  demonstrates advantages of combining these activities in the context of system performance optimization. Simulation optimization algorithms can be improved by exploiting the ability to observe and change literally  anything  at any time while a simulation is running. It is also not necessary to stop simulating candidates for the optimal system before starting to simulate others. The ability to observe and change many concurrently running simulated systems considerably expands the possibilities for designing simulation experiments. Examples are presented for a range of simulation optimization algorithms including randomized search, directional search, pattern search, and agent-based particle swarm optimization.
['Lee W. Schruben']
I am writing a research paper on a idea Simulation modeling, experimenting, analysis, and implementation
[{"from": "human", "value": "I am writing a research paper on a idea Simulation modeling, experimenting, analysis, and implementation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Textbooks sometimes describe building models, running experiments, analyzing outputs, and implementing results as distinct activities in a simulation project.  demonstrates advantages of combining these activities in the context of system performance optimization. Simulation optimization algorithms can be improved by exploiting the ability to observe and change literally  anything  at any time while a simulation is running. It is also not necessary to stop simulating candidates for the optimal system before starting to simulate others. The ability to observe and change many concurrently running simulated systems considerably expands the possibilities for designing simulation experiments. Examples are presented for a range of simulation optimization algorithms including randomized search, directional search, pattern search, and agent-based particle swarm optimization."}]