Simulation modelling has an ever increasing importance for complex systems. Manufacturing and related material flow or logistic systems are typical fields of application. Latest trends such as Cyber-physical systems and Industry 4.0 give a significant boost to simulation modelling as these require a digital model of the system. Complex manufacturing and related material flow systems are subject to frequent changes and pose a Big Data problem, which raises stronger requirements regarding self-adaptiveness. Conventional simulation models are to be adapted only via user interaction. Previous research steps have concentrated on the establishment of a novel simulation model structure, the so called "Jellyfish" model which unifies layout and process-type simulation models. Visualization of both aspects simultaneously enables interacting users to better understand the systems' operation compared to the conventional models. The current paper focuses on the adaptive capability of the new model. We have concentrated on the hardest type of adaptation, the structural adaptation. In this paper, an ontology-driven component based approach is presented and explained further through an example. Application of automated ontology-matching in simulation environment is a novel approach enabling the simulation model to adapt its structure without the necessity of manual interaction.
The process description languages are used in the business may be useful in the optimization of logistics processes too. The process description languages would be the obvious solution for process control, to handle the main sources of faults and to give a correct list of what to do during the logistics process. Related to this, firstly, the paper presents the main features of the frequent process description languages. The following section describes the currently most used process modelling languages, in the areas of production and construction logistics. In addition, the paper gives some examples of logistics simulation, as another very important field of logistics system modelling. The main edification of the paper, the logistics simulation supported by process description languages. The paper gives a comparison of a Petri net formal representation and a Simul8 model, through a construction logistics model, as the major contribution of the research.
Application of agent based approaches is frequent in systems with artificial intelligence. This method has been used in simulations as well, however application in this field is rather novel. The paper surveys formation possibilities of agent based structures in the simulation of an example material flow systems. Further, considerations are also presented, how these models can be made adaptive, which makes them capable of continuously modify their features to the modeled physical system. Finally a framework for a proposed modeling problem is introduced.
Figure 1. Deviation of the KPI values of the system and the model over time Figure 2. Deviation of the KPI values of the system and the model over time with regular update Abstract-This paper summarizes and analyses simulation techniques for material flow systems. The analysis deals with primary the applicability of various simulation methods for the purpose of adaptive simulation models.
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