@phdthesis{Lesch2022,
  author    = {Lesch, Veronika},
  title     = {Self-Aware Optimization of Cyber-Physical Systems in Intelligent Transportation and Logistics Systems},
  doi       = {10.25972/OPUS-27228},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-272285},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {In today's world, circumstances, processes, and requirements for systems in general-in this thesis a special focus is given to the context of Cyber-Physical Systems (CPS)-are becoming increasingly complex and dynamic. In order to operate properly in such dynamic environments, systems must adapt to dynamic changes, which has led to the research area of Self-Adaptive Systems (SAS). These systems can deal with changes in their environment and the system itself. In our daily lives, we come into contact with many different self-adaptive systems that are designed to support and improve our way of life. In this work we focus on the two domains Intelligent Transportation Systems (ITS) and logistics as both domains provide complex and adaptable use cases to prototypical apply the contributions of this thesis. However, the contributions are not limited to these areas and can be generalized also to other domains such as the general area of CPS and Internet of Things including smart grids or even intelligent computer networks. In ITS, real-time traffic control is an example adaptive system that monitors the environment, analyzes observations, and plans and executes adaptation actions. Another example is platooning, which is the ability of vehicles to drive with close inter-vehicle distances. This technology enables an increase in road throughput and safety, which directly addresses the increased infrastructure needs due to increased traffic on the roads. In logistics, the Vehicle Routing Problem (VRP) deals with the planning of road freight transport tours. To cope with the ever-increasing transport volume due to the rise of just-in-time production and online shopping, efficient and correct route planning for transports is important. Further, warehouses play a central role in any company's supply chain and contribute to the logistical success. The processes of storage assignment and order picking are the two main tasks in mezzanine warehouses highly affected by a dynamic environment. Usually, optimization algorithms are applied to find solutions in reasonable computation time. SASes can help address these dynamics by allowing systems to deal with changing demands and constraints. For the application of SASes in the two areas ITS and logistics, the definition of adaptation planning strategies is the key success factor. A wide range of adaptation planning strategies for different domains can be found in the literature, and the operator must select the most promising strategy for the problem at hand. However, the No-Free-Lunch theorem states that the performance of one strategy is not necessarily transferable to other problems. Accordingly, the algorithm selection problem, first defined in 1976, aims to find the best performing algorithm for the current problem. Since then, this problem has been explored more and more, and the machine learning community, for example, considers it a learning problem. The ideas surrounding the algorithm selection problem have been applied in various use cases, but little research has been done to generalize the approaches. Moreover, especially in the field of SASes, the selection of the most appropriate strategy depends on the current situation of the system. Techniques for identifying the situation of a system can be found in the literature, such as the use of rules or clustering techniques. This knowledge can then be used to improve the algorithm selection, or in the scope of this thesis, to improve the selection of adaptation planning strategies. In addition, knowledge about the current situation and the performance of strategies in similar previously observed situations provides another opportunity for improvements. This ongoing learning and reasoning about the system and its environment is found in the research area Self-Aware Computing (SeAC). In this thesis, we explore common characteristics of adaptation planning strategies in the domain of ITS and logistics presenting a self-aware optimization framework for adaptation planning strategies. We consider platooning coordination strategies from ITS and optimization techniques from logistics as adaptation planning strategies that can be exchanged during operation to better reflect the current situation. Further, we propose to integrate fairness and uncertainty handling mechanisms directly into the adaptation planning strategies. We then examine the complex structure of the logistics use cases VRP and mezzanine warehouses and identify their systems-of-systems structure. We propose a two-stage approach for vertical or nested systems and propose to consider the impact of intertwining horizontal or coexisting systems. More specifically, we summarize the six main contributions of this thesis as follows: First, we analyze specific characteristics of adaptation planning strategies with a particular focus on ITS and logistics. We use platooning and route planning in highly dynamic environments as representatives of ITS and we use the rich Vehicle Routing Problem (rVRP) and mezzanine warehouses as representatives of the logistics domain. Using these case studies, we derive the need for situation-aware optimization of adaptation planning strategies and argue that fairness is an important consideration when applying these strategies in ITS. In logistics, we discuss that these complex systems can be considered as systems-of-systems and this structure affects each subsystem. Hence, we argue that the consideration of these characteristics is a crucial factor for the success of the system. Second, we design a self-aware optimization framework for adaptation planning strategies. The optimization framework is abstracted into a third layer above the application and its adaptation planning system, which allows the concept to be applied to a diverse set of use cases. Further, the Domain Data Model (DDM) used to configure the framework enables the operator to easily apply it by defining the available adaptation planning strategies, parameters to be optimized, and performance measures. The framework consists of four components: (i) Coordination, (ii) Situation Detection, (iii) Strategy Selection, and (iv) Parameter Optimization. While the coordination component receives observations and triggers the other components, the situation detection applies rules or clustering techniques to identify the current situation. The strategy selection uses this knowledge to select the most promising strategy for the current situation, and the parameter optimization applies optimization algorithms to tune the parameters of the strategy. Moreover, we apply the concepts of the SeAC domain and integrate learning and reasoning processes to enable ongoing advancement of the framework. We evaluate our framework using the platooning use case and consider platooning coordination strategies as the adaptation planning strategies to be selected and optimized. Our evaluation shows that the framework is able to select the most appropriate adaptation strategy and learn the situational behavior of the system. Third, we argue that fairness aspects, previously identified as an important characteristic of adaptation planning strategies, are best addressed directly as part of the strategies. Hence, focusing on platooning as an example use case, we propose a set of fairness mechanisms to balance positive and negative effects of platooning among all participants in a platoon. We design six vehicle sequence rotation mechanisms that continuously change the leader position among all participants, as this is the position with the least positive effects. We analyze these strategies on roads of different sizes and with different traffic volumes, and show that these mechanisms should also be chosen wisely. Fourth, we address the uncertainty characteristic of adaptation planning strategies and propose a methodology to account for uncertainty and also address it directly as part of the adaptation planning strategies. We address the use case of fueling planning along a route associated with highly dynamic fuel prices and develop six utility functions that account for different aspects of route planning. Further, we incorporate uncertainty measures for dynamic fuel prices by adding penalties for longer travel times or greater distance to the next gas station. Through this approach, we are able to reduce the uncertainty at planning time and obtain a more robust route planning. Fifth, we analyze optimization of nested systems-of-systems for the use case rVRP. Before proposing an approach to deal with the complex structure of the problem, we analyze important constraints and objectives that need to be considered when formulating a real-world rVRP. Then, we propose a two-stage workflow to optimize both systems individually, flexibly, and interchangeably. We apply Genetic Algorithms and Ant Colony Optimization (ACO) to both nested systems and compare the performance of our workflow with state-of-the-art optimization algorithms for this use case. In our evaluation, we show that the proposed two-stage workflow is able to handle the complex structure of the problem and consider all real-world constraints and objectives. Finally, we study coexisting systems-of-systems by optimizing typical processes in mezzanine warehouses. We first define which ergonomic and economic constraints and objectives must be considered when addressing a real-world problem. Then, we analyze the interrelatedness of the storage assignment and order picking problems; we identify opportunities to design optimization approaches that optimize all objectives and aim for a good overall system performance, taking into account the interdependence of both systems. We use the NSGA-II for storage assignment and Ant Colony Optimization (ACO) for order picking and adapt them to the specific requirements of horizontal systems-of-systems. In our evaluation, we compare our approaches to state-of-the-art approaches in mezzanine warehouses and show that our proposed approaches increase the system performance. Our proposed approaches provide important contributions to both academic research and practical applications. To the best of our knowledge, we are the first to design a self-aware optimization framework for adaptation planning strategies that integrates situation-awareness, algorithm selection, parameter tuning, as well as learning and reasoning. Our evaluation of platooning coordination shows promising results for the application of the framework. Moreover, our proposed strategies to compensate for negative effects of platooning represent an important milestone, which could lead to higher acceptance of this technology in society and support its future adoption in the real world. The proposed methodology and utility functions that address uncertainty are an important step to improving the capabilities of SAS in an increasingly turbulent environment. Similarly, our contributions to systems-of-systems optimization are major contributions to the state of logistics and systems-of-systems research. Finally, we select real-world use cases for the application of our approaches and cooperate with industrial partners, which highlights the practical relevance of our contributions. The reduction of manual effort and required expert knowledge in our self-aware optimization framework is a milestone in bridging the gap between academia and practice. One of our partners integrated the two-stage approach to tackling the rVRP into its software system, improving both time to solution and solution quality. In conclusion, the contributions of this thesis have spawned several research projects such as a long-term industrial project on optimizing tours and routes in parcel delivery funded by Bayerisches Verbundforschungsprogramm (BayVFP) - Digitalisierung and further collaborations, opening up many promising avenues for future research.},
  subject      = {Mehrkriterielle Optimierung},
  language  = {en}
}