![]() Simulating a simple system for which we can find an exact analytical solution only adds uncertainty to the results, making them less precise."Ĭhapter 1.4 of: Simio and Simulation (Jeffrey S. ![]() Instead, exact analytical methods like queueing theory, probability, or simple algebra or calculus could do the job. One of the main reasons for this is embodied in that word “complicated.” If the system of interest were actually simple enough to be validly represented by an exact analytical model, simulation wouldn’t be needed, and indeed shouldn’t be used. "Simulation of complicated systems has become quite popular. Both DES and DTS require capacity, like machine count and kanban count, as inputs. Users and researchers have discussed the pros and cons of the different techniques: DES requiring more time and suffering from inaccuracy when utilizations get high DTS having lower accuracy in general, especially with low utilizations, and the challenges of deciding on the right time step. Popular approaches include discrete event simulation (DES), continuous simulations that use small time steps to loop through non-discrete changes (also known as discrete-time simulation or DTS), and agent-based models which may be based on either DES or DTS. Using sampling, problems can be solved numerically that are too complex to be solved analytically, as had long been the case for production system dynamics. Monte Carlo means that the simulation randomly plays through possible sequences of events hundreds of times (sampling) to statistically approximate the overall system performance. In any of these software tools, simulating the behavior and dynamics of complex production systems involves Monte Carlo methods. ![]() Popular brand names include AnyLogic, Arena, FlexSim, ProModel, Simio, Tecnomatix, and many others. The most common tool to achieve this task today is discrete event simulation, which is available in a variety of software packages. A fundamental goal of production simulation is to make predictions about how production capacity and flow behavior influence factory performance.
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