Simulation models are important for planing, implementing and operating logistics systems since they can depict their dynamic system behavior. In the field of logistics, discrete-event models are widely used. Their creation and computation is often very time and labor consuming. For this reason, the paper presents a new mesoscopic modeling and simulation approach to quickly and effectively execute analysis and planning tasks related to production and logistics systems. Mesoscopic models represent logistics flow processes on an aggregated level through piecewise constant flow rates instead of modeling individual flow objects. The results are not obtained by counting individual objects but by using mathematical formulas to calculate the results as continuous quantities in every modeling time step. This leads to a fast model creation and computation. In terms of level of detail, mesoscopic simulation models fall between object based discrete-event simulation models and flow based continuous simulation models.

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A mesoscopic approach to modeling and simulation of logistics processes

This paper presents a mesoscopic simulation approach in addition to the prevailing continuous and discrete event simulation approaches used for logistics systems. In terms of level of detail, the mesoscopic approach falls between these two approaches. Mesoscopic models represent logistics flow processes through piecewise constant flow rates. The resulting linearity of the cumulative flows allows for event scheduling and the use of mathematical formulas for recalculating the system‘s state variables at every simulation time step. The simulation time step is variable and the step size depends on the occurrence of scheduled events. This leads to a high computational performance. The mesoscopic approach distinguishes between different parallel product types. The modeling components are multichannel funnels, delays, assemblies and disassemblies.

A Mesoscopic Approach to the Simulation of Logistics Systems

It is well-known that semiconductor manufacturing is one of the most complex types of production. Consequently, making the right decisions in order to get the optimum output of a semiconductor fab is quite challenging. For that reason, sophisticated discrete event simulation studies may be seen as a kind of magic tool for fab managers to face the ever-increasing complexity. But building complex dynamic simulation models in order to solve complex problems is not always the best solution. This article gives an overview of the discussion about the correct level of complexity of discrete event simulation models and lists the main reasons for the creation of overcomplex models. To provide guidance for practitioners, lessons learned are derived from four "real world" use cases in which the correct level of complexity was a major issue.

http://ieeexplore.ieee.org/iel7/7486622/7491073/07491145.pdf

The correct level of model complexity in semiconductor fab simulation — Lessons learned from practice

Mesoscopic Simulation Models for Logistics Planning Tasks in the Automotive Industry

In this paper a method is presented for mapping the simulation state between models of different level of detail in dynamic multiresolution modeling of discrete event material flow systems. In dynamic multiresolution modeling it is possible to inactivate a model part of certain detail and activate a model of higher or lower detail representing the same part of the system during simulation. To allow a consistent simulation, the state of the inactivated model has to be mapped to the activated model. The mapping is done by first mapping the jobs, then mapping the breakdown status and statistical variables. Experiments show that consistent states are generated, such that the simulation continues after state mapping. The errors generated by mapping are small and caused by the loss of information in the models of lower detail.

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A method for simulation state mapping between discrete event material flow models of different level of detail

/pdf/controlled-simplification-of-material-flow-simulation-models-1eelchhqsh.pdf

Huber

Papers

Controlled simplification of material flow simulation models