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Many complex real world systems can be modeled as discrete-event systems (DES). Examples are computer-communication networks, flexible manufacturing systems, probabilistic fracture mechanics models, PERT-project networks and flow networks. In view of the complex interactions within a DES, they are typically studied via stochastic simulation.
In designing and analyzing a DES, we are often interested not only in performance evaluation, but in sensitivity analysis and optimization as well. Consider for example manufacturing systems. Here (i) the performance measure may be the average waiting time of an item to be processed at several work stations (robots) according to a given schedule and route; (ii) the sensitivity and decision parameters may be the average rate at which the work-stations (robots) process the item. In such a system, we might be interested in minimizing the average makespan consisting of the processing time and delay time with allowance for some constraints (e.g., cost).
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