Advanced Concepts in Simulation
Computer simulation has not been used on a professional scale until after World War II, and also then mostly for military uses like war games or simulations of atomic bomb explosions nowadays.
One of the first scientific papers on simulation has been published in the late 70s by Ören and Zeigler. They aggregate some fundamental knowledge about simulation and suggest an conceptual model for simulation, which I want to introduce today from the perspective of a supply chain
Components of Simulation
Simulation programs can be described completely using the following terms:
- Model Structure, describes the static and dynamic aspects of the model, like components of the model, variables and rules for interaction (supply chain context: echelons and elements of the echelon)
- Model Outputs, contain all variables and functions that can be observed during and after execution (eg. lead times, profit of the chain)
- Input Scheduling, when is the model fed with what inputs (eg. how is demand generated)
- Initialization of Simulation, what are the starting parameters of the model (eg. are queues already loaded?)
- Termination of Simulation, what are the conditions for model termination, may be after specific time or specific model states
- Data Collection, how and when is the data during simulation collected
- Simulator, the simulator carries out the model’s instructions to generate the new model states (nowadays the Simulator is usually a software like Arena, Anylogic or similar)
All those elements can be found within modern simulation tools, since they do not only include the Simulator component, but also aids to model and control the supply chain.
Simulation Models can be further characterized by the “language” they are using. First, is the model using a continuous or discrete time scale. Second, orientation the model: block- or expression-oriented; and last, what is the world view expressed in the model by differential equations, events or process interactions.
After modeling a supply chain you can do experiments using the model, but of course also in reality (eg. increasing buffers or changing inventory policies). This would allow the experimenter to validate and compare the models output with the real changes. Of course in a supply chain context experiments in reality are confined to a very small range of parameter variation without compromising the ongoing operations.
This discussion focusses on the object of the experiment. But there are other components of an experiment. The experimental frame is defined as
a limited set of circumstances under which the system is to be observed or subject to experimentation,so the settings which are used for above mentioned components of a simulation.
Advanced Concepts and Conclusion
The authors suggest some concepts for advancing simulation which are standard in nowadays simulation software. For example they describe a closer linking of model and experiment or demand capabilities for easy design of models and experiments.
So after all a very insightful article in the historical development of simulation. This has not been discussed here, but of course there are pros and cons for simulation. There are strong opponents of using simulation in the supply chain context (eg. Sodhi and Tang, 2009), since at least when using optimization there will always be an optimality gap. Instead mathematical modeling should be used.
But the huge amount of literature using different kinds of simulation (eg. reviewed here) shows that supply chain management can benefit very much from simulation, since the method is probably more easily understood, than some mathematical models.
Oren, T., & Zeigler, B. (1979). Concepts for advanced simulation methodologies SIMULATION, 32 (3), 69-82 DOI: 10.1177/003754977903200301