An incomplete glossary about simulation

No term mentionned below is really specific to the simulation of flow, which is not based on a particular mathematical or philosophical theory – which precisely characterizes it. It seemed useful to us to explain certain expressions which often come up in our technical jargon, and maybe on this site. And rather than adopting the alphabetical order, we chose one that almost allows for a linear and meaningful reading…

Simulation (flow simulation): it is a quantitative method for studying the behavior over time of a model in which flows circulate, and submitted to various hypotheses. The approach is empirical, because it is through successive experiments (simulations) that the assumptions will be contradicted or confirmed.

Modeling: it means representing a real system with enough accuracy for the model to be able to reproduce the operation of the system and to predict its behavior under other conditions. Here we are building a computer model.

Simulating: to simulate is to activate the model over time, based on hypotheses that we want to study. It is an approach made up of experiments on the model and its dynamic behavior.

Replication: each experiment done on the model, with a different data set, is called a replication. The flow simulation approach involves performing several replications, to test the variation of each parameter, independently or jointly. Sometimes it is the presence of random values ​​that requires increasing the number of replications, so as not to make decisions from 2 or 3 random draws. It still happens that the scenarios to be tested do not differ only in the input data, but in the functional architecture of parts of the model: there will thus be several variants of the model, each of which will be simulated with several replications.

Discrete: we use the term “discrete flows” (of which we can count the elements) as opposed to continuous flows (which seem to us fluid and measurable only by rates). We also speak of discrete event simulation: it is the type of simulation that applies when we are interested in the organization of a system (industrial or other), because it is described by events (arrival of an order, end time etc.) and successions of operations of singular duration (duration of the phone call, conveying then machining operation etc.).

Continuous: it is the other world, where what happens to the flow can be expressed in the form of differential equations describing the evolution over time of what we observe (heat, density, throughput, euros).

Hybrid: we could also say “mixed”, when the continuous mixes with the discrete in a same flow, as for example in a workshop that produces lemonade. Here the flow is a liquid, transformed by a “recipe”, but what comes out are bottles that had to be washed, filled, corked, labeled, packed.

History (very shortened): flow simulation owes its development to the application of system dynamics (Forrester, 1961 and later) to various economic fields (including industrial engineering), as well as to the development of adequate and accessible software (i.e. not too expensive, not too complicated). For four decades, all the major industrial groups have used simulation to develop their production systems; for two decades, the approach has become more democratic and has been used in SMEs as well as by consultants; in the last fifteen years it has increasingly affected the tertiary sector, flows outside industry and flows of people.

Dynamic: The approach could be called dynamic flow simulation, because it is always a question of studying over time the evolution of phenomena which are precisely difficult to apprehend with some static tool (self-regulation according to a context, feedback, random phenomena).

Stochastic: same as “random”, but more classy to place in a meal with friends. In reality, random is said for a variable, while stochastic is applied to a process or a function, that is, something that changes over time. Flow simulation software can represent stochastic phenomena in models, and this is very important because we find everywhere random arrivals of customers or breakdowns, durations that obey distribution laws, or human factors to take into account.

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