This thesis focuses on control strategies for intralogistics transport systems. It evaluates how switching from central to decentral dispatching approaches influences the performance of these systems. Many ideas and prototypes for implementing decentral control have been suggested by the scientific community. But usually only the qualitative advantages of this new paradigm are stated. The impact on the performance is not quantified and analyzed. Additionally, decentral control is often confused with distributed algorithms or uses the aggregation of local to global information. In the case of the latter, the technological limitations due to the communication overhead are not considered. The decentral prototypes usually only focus on routing. This paper takes a step back and provides a generic simulation environment which can be used by other researchers to test and compare control strategies in the future. The test environment is used for developing four truly decentral dispatching strategies which work only based on local information. These strategies are compared to a central approach for controlling transportation systems. Input data from two real-world applications is used for a series of simulation experiments with three different layout complexities. Based on the simulation studies neither the central nor the decentral dispatching strategies show a universally superior performance. The results depend on the combination of input data set and layout scenario. The expected efficiency loss for the decentral approaches can be confirmed for stable input patterns. Regardless of the layout complexity the decentral strategies always need more vehicles to reach the performance level of the central control rule when these input characteristics are present. In the case of varying input data and high throughput the decentral strategies outperform the central approach in simple layouts. They require fewer vehicles and less vehicle movement to achieve the central performance. Layout simplicity makes the central dispatching strategy prone to undesired effects. The simple-minded decentral decision rules can achieve a better performance in this kind of environment. But only complex layouts are a relevant benchmark scenario for transferring decentral ideas to real-world applications. In such a scenario the decentral performance deteriorates while the layout-dependent influences on the central strategy become less relevant. This is true for both analyzed input data sets. Consequently, the decentral strategies require at least 36% to 53% more vehicles and 20% to 42% more vehicle movement to achieve the lowest central performance level. Therefore their usage can currently not be justified based on investment and operating costs. The characteristics of decentral systems limit their own performance. The restriction to local information leads to poor dispatching decisions which in return induce self-enforcing inefficiencies. In addition, the application of decentral strategies requires bigger storage location capacity. In several disturbance scenarios the decentral strategies perform fairly well and show their ability to adapt to changed environmental conditions. However, their performance after the disturbance remains in some cases unpredictable and relates to the properties of self-organizing complex systems. A real-world applicability has to be called into question.

This work talks about decentralized rainwater management. It describes the ways of decentralized management and looks into the issue from the legislation point of view. In the practical part, a computational tool was created to make it easier to decide how much tank to choose depending on the size of the collecting area and the irrigated area.

This thesis focuses on control strategies for intralogistics transport systems. It evaluates how switching from central to decentral dispatching approaches influences the performance of these systems. Many ideas and prototypes for implementing decentral control have been suggested by the scientific community. But usually only the qualitative advantages of this new paradigm are stated. The impact on the performance is not quantified and analyzed. Additionally, decentral control is often confused with distributed algorithms or uses the aggregation of local to global information. In the case of the latter, the technological limitations due to the communication overhead are not considered. The decentral prototypes usually only focus on routing. This paper takes a step back and provides a generic simulation environment which can be used by other researchers to test and compare control strategies in the future. The test environment is used for developing four truly decentral dispatching strategies which work only based on local information. These strategies are compared to a central approach for controlling transportation systems. Input data from two real-world applications is used for a series of simulation experiments with three different layout complexities. Based on the simulation studies neither the central nor the decentral dispatching strategies show a universally superior performance. The results depend on the combination of input data set and layout scenario. The expected efficiency loss for the decentral approaches can be confirmed for stable input patterns. Regardless of the layout complexity the decentral strategies always need more vehicles to reach the performance level of the central control rule when these input characteristics are present. In the case of varying input data and high throughput the decentral strategies outperform the central approach in simple layouts. They require fewer vehicles and less vehicle movement to achieve the central performance. Layout simplicity makes the central dispatching strategy prone to undesired effects. The simple-minded decentral decision rules can achieve a better performance in this kind of environment. But only complex layouts are a relevant benchmark scenario for transferring decentral ideas to real-world applications. In such a scenario the decentral performance deteriorates while the layout-dependent influences on the central strategy become less relevant. This is true for both analyzed input data sets. Consequently, the decentral strategies require at least 36% to 53% more vehicles and 20% to 42% more vehicle movement to achieve the lowest central performance level. Therefore their usage can currently not be justified based on investment and operating costs. The characteristics of decentral systems limit their own performance. The restriction to local information leads to poor dispatching decisions which in return induce self-enforcing inefficiencies. In addition, the application of decentral strategies requires bigger storage location capacity. In several disturbance scenarios the decentral strategies perform fairly well and show their ability to adapt to changed environmental conditions. However, their performance after the disturbance remains in some cases unpredictable and relates to the properties of self-organizing complex systems. A real-world applicability has to be called into question.