Computer simulation of dynamic systems
Computer simulation of dynamic systems
Genetic Algorithms
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Monte Carlo techniques have long been used (since Buffon's experiment to approximate the value of π by tossing a needle onto striped paper) to analyze phenomena which, due to their complexity and/or stochasticity, are beyond the reach of closed-form equations. Basic examples of such studies are estimating the probability that military field communications will remain intact in the face of attack or the number of fish in an irregularly shaped lake. Likewise, scheduling is a necessity for the planning, control, and implementation of increasingly large projects in manufacturing, civil construction, military operations, and many other fields. We provide a framework for applying scheduling algorithms based on Monte Carlo simulation, to provide a scheduler, who inevitably confronts numerous uncertainties, an inexpensive and a highly customizable tool that can be utilized in a common spreadsheet environment.