In traffic networks, appropriately determining the traffic signal plan of each intersection is a ünecessary condition for a reasonable level of service. This paper presents the development of a new system for automatically designing optimal actuated traffic signal plans with transit signal priority. It uses an optimization algorithm combined with a mesoscopic traffic simulation model to design and evaluate optimal traffic signal plans for each intersection in the traffic network, therefore reducing the need for human intervention in the design process. The proposed method can simultaneously determine the optimal logical structure, priority strategies, timing parameters, phase composition and sequence, and detector placements. The integrated system was tested by a real-world isolated intersection in Haifa city. The results demonstrated that this approach has the potential to efficiently design signal plans without human intervention, which can minimize time, cost, and design effort. It can also help uncover problems in the design that may otherwise not be detected.

Intelligente Technologien der Gebäude- und Raumautomation (RA) werden als essentiell eingeschätzt, um im Gebäudesektor Energie zu sparen und Treibhausemissionen wie CO2 zu verringern. Komplexe Planungsprozesse und ein unübersichtliches Angebot Tausender RA-Produkte hemmen jedoch die Verbreitung von RA-Systemen. Als potentielle Vereinfachung wurden auf Basis funktionaler Modelle von RA-Produkten automatische Entwurfsansätze – wie der an der TU Dresden entwickelte automatische Systementwurf „AUTERAS“ – erforscht, deren praktischer Nutzen bisher allerdings nicht hinreichend demonstriert werden konnte. Die Hauptursache dafür liegt daran, dass sich die existierenden Modellierungsansätze für funktionale Komponentenmodelle in der Praxis nicht bewährt haben. Um eine Verbreitung von automatischen Entwurfsansätzen für RA in der Praxis zu ermöglichen, entwickelt diese Arbeit mit dem Modellierungsansatz „BA-GSem“ ein praxistaugliches, funktionales Komponentenmodell, welches einerseits detaillierte Modellstrukturen und andererseits fortgeschrittene Konzepte zur Vereinfachung des Modellierungsprozesses bereitstellt. Die von BA-GSem definierten Modellstrukturen umfassen mit Teilmodellen für Komponentenstrukturen, Funktionalität und Schnittstellensemantik verschiedene Aspekte von RA-Produkten, wodurch die wesentlichen Teilschritte des Entwurfsprozesses von RA-Systemen in hoher Qualität automatisiert werden können. Dabei wird die Funktionalität von Produkten durch Funktionalitäts- und Informationsflussgraphen formal auf Ebene des Informationstransports und der Informationsverarbeitung modelliert. Zusätzlich führt BA-GSem ein modulares Klassifikationsframework für Semantik ein, das auf Basis eines Vokabulars von semantischen Annotationen (sog. „Tags“) komplexe Semantikausdrücke formen und dadurch Informationstypen feingranular repräsentieren kann. Ergänzend zu den Modellstrukturen von BA-GSem wird das Forschungsgebiet „Modellierungsunterstützung für funktionale Komponentenmodelle“ untersucht, um Konzepte zur Senkung des Modellierungsaufwands zu ermitteln. Exemplarisch realisierte Vorschlagsfunktionen für Modellelemente erweisen sich als geeignet, um die Komplexitätstreiber des Modellierungsprozesses beherrschen zu können. Durch die Ergebnisse dieser Arbeit wird einerseits die Qualität der mit automatischen Entwurfsansätzen erhaltenen Ergebnisse deutlich verbessert. Andererseits kann eine signifikante Reduktion des Modellierungsaufwands von RA-Produkten erreicht werden. Damit verringert BA-GSem eine wesentliche Hürde für die zukünftige Etablierung von automatischen Entwurfsansätzen in der Branche der Raumautomation und trägt somit mittelbar zu Energieeinsparungen im Gebäudesektor bei.:Einleitung Grundlagen Stand der Technik Lösungskonzept BA-GSem: Komponentenmodell Einsatzmöglichkeiten des BA-GSem Komponentenmodells Erstellung von Komponentenmodellen Bewertung der Lösung Zusammenfassung und Ausblick / Intelligent technologies in the field of building and room automation (RA) are considered essential for reducing the energy consumption and greenhouse gas emissions in the building sector. However, engineering RA systems is a complex task, which is even further exacerbated by the myriad of thousands of RA products available to planners and system integrators. Approaches for an automated engineering of RA systems – such as the “AUTERAS” approach developed at the TU Dresden – have been proposed as potential facilitators for a more efficient engineering. These approaches make use of formal models for product functionality to solve the complex task of product selection. But, since the existing approaches for functionality modelling have not proven their effectiveness in practice, the usefulness of the automated engineering approaches has not yet been demonstrated. In order to aid the practical proliferation of automated engineering for RA systems, this thesis contributes an approach for functional modelling called “BA-GSem”, which consists of detailed model structures and advanced concepts to reduce the effort of the modelling process. The model structures defined by BA-GSem address the aspects of component structures, functionality, and interface semantics. With these model structures in place, the main tasks of engineering RA systems can be automated, yielding high quality results. To achieve this, functionality is modeled as a graph of functionalities and information flows to express the transport and transformation of information in an RA product. In addition, BA-GSem proposes a modular framework for classification of semantics. This framework uses a vocabulary of semantic annotations (“tags”) to build complex semantic expressions, which are able to represent fine-grained classes of information types. BA-GSem also investigates the research area of “modelling support for functional component models” for reducing the effort of the modelling process, thereby complementing the detailed model structures proposed. Suggestion functionalities as proof-of-concept realizations of modelling support seem capable of coping with complexity-critical modelling tasks. As a result, the contributions of this thesis improve the quality of the results of automated engineering approaches. Furthermore, it enables a significant reduction of the modelling effort for RA products. Both effects reduce a major barrier for establishing automated engineering approaches in the room automation domain and for unlocking the potential contributions of the building sector towards achieving the world’s climate goals.:Einleitung Grundlagen Stand der Technik Lösungskonzept BA-GSem: Komponentenmodell Einsatzmöglichkeiten des BA-GSem Komponentenmodells Erstellung von Komponentenmodellen Bewertung der Lösung Zusammenfassung und Ausblick

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.:1 Introduction 17 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 1.3 Goals and Use of the Thesis . . . . . . . . . . . . . . . . . . . . . 21 1.4 Solution Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . 24 2 Design Creation for Building Automation Systems 25 2.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 2.2 Engineering of Building Automation Systems . . . . . . . . . . . 29 2.3 Network Protocols of Building Automation Systems . . . . . . . 33 2.4 Existing Solutions for Design Creation . . . . . . . . . . . . . . . 34 2.5 The Device Interoperability Problem . . . . . . . . . . . . . . . . 37 2.6 Guidelines for Planning of Room Automation Systems . . . . . . 38 2.7 Quality Requirements on BAS . . . . . . . . . . . . . . . . . . . 41 2.8 Quality Requirements on Design . . . . . . . . . . . . . . . . . . 42 2.8.1 Quality Requirements Related to Project Planning . . . . 42 2.8.2 Quality Requirements Related to Project Implementation 43 2.9 Quality Requirements on Methods . . . . . . . . . . . . . . . . . 44 2.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3 The Design Creation Task 47 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.2 System Design Composition Model . . . . . . . . . . . . . . . . . 49 3.2.1 Abstract and Detailed Design Model . . . . . . . . . . . . 49 3.2.2 Mapping Model . . . . . . . . . . . . . . . . . . . . . . . . 51 3.3 Formulation of the Problem . . . . . . . . . . . . . . . . . . . . . 53 3.3.1 Problem properties . . . . . . . . . . . . . . . . . . . . . . 54 3.3.2 Requirements on Algorithms . . . . . . . . . . . . . . . . 56 3.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4 Solution Methods for Design Generation and Optimization 59 4.1 Combinatorial Optimization . . . . . . . . . . . . . . . . . . . . . 59 4.2 Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.3 Examples for Metaheuristics . . . . . . . . . . . . . . . . . . . . . 62 4.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . 62 4.3.2 Tabu Search . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3 Ant Colony Optimization . . . . . . . . . . . . . . . . . . 65 4.3.4 Evolutionary Computation . . . . . . . . . . . . . . . . . 66 4.4 Choice of the Solver Algorithm . . . . . . . . . . . . . . . . . . . 69 4.5 Specialized Methods for Diversity Preservation . . . . . . . . . . 70 4.6 Approaches for Real World Problems . . . . . . . . . . . . . . . . 71 4.6.1 Component-Based Mapping Problems . . . . . . . . . . . 71 4.6.2 Network Design Problems . . . . . . . . . . . . . . . . . . 73 4.6.3 Comparison of Solution Methods . . . . . . . . . . . . . . 74 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5 Automated Creation of Optimized Designs 79 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2 Design Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.3 Component Model . . . . . . . . . . . . . . . . . . . . . . . . . . 81 5.3.1 Presumptions . . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3.2 Integration of Component Model . . . . . . . . . . . . . . 87 5.4 Design Generation . . . . . . . . . . . . . . . . . . . . . . . . . . 87 5.4.1 Component Search . . . . . . . . . . . . . . . . . . . . . . 88 5.4.2 Generation Approaches . . . . . . . . . . . . . . . . . . . 100 5.5 Design Improvement . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.5.1 Problems and Requirements . . . . . . . . . . . . . . . . . 107 5.5.2 Variations . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 5.5.3 Application Strategies . . . . . . . . . . . . . . . . . . . . 121 5.6 Realization of the Approach . . . . . . . . . . . . . . . . . . . . . 122 5.6.1 Objective Functions . . . . . . . . . . . . . . . . . . . . . 122 5.6.2 Individual Representation . . . . . . . . . . . . . . . . . . 123 5.7 Automated Design Creation For A Building . . . . . . . . . . . . 124 5.7.1 Room Spanning Control . . . . . . . . . . . . . . . . . . . 124 5.7.2 Flexible Rooms . . . . . . . . . . . . . . . . . . . . . . . . 125 5.7.3 Technology Spanning Designs . . . . . . . . . . . . . . . . 129 5.7.4 Preferences for Mapping of Function Blocks to Devices . . 132 5.8 Further Uses and Applicability of the Approach . . . . . . . . . . 133 5.9 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6 Validation and Performance Analysis 137 6.1 Validation Method . . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 137 6.3 Example Abstract Designs and Performance Tests . . . . . . . . 139 6.3.1 Criteria for Choosing Example Abstract Designs . . . . . 139 6.3.2 Example Abstract Designs . . . . . . . . . . . . . . . . . . 140 6.3.3 Performance Tests . . . . . . . . . . . . . . . . . . . . . . 142 6.3.4 Population Size P - Analysis . . . . . . . . . . . . . . . . 151 6.3.5 Cross-Over Probability pC - Analysis . . . . . . . . . . . 157 6.3.6 Mutation Probability pM - Analysis . . . . . . . . . . . . 162 6.3.7 Discussion for Optimization Results and Example Designs 168 6.3.8 Resource Consumption . . . . . . . . . . . . . . . . . . . . 171 6.3.9 Parallelism . . . . . . . . . . . . . . . . . . . . . . . . . . 172 6.4 Optimization Framework . . . . . . . . . . . . . . . . . . . . . . . 172 6.5 Framework Design . . . . . . . . . . . . . . . . . . . . . . . . . . 174 6.5.1 Components and Interfaces . . . . . . . . . . . . . . . . . 174 6.5.2 Workflow Model . . . . . . . . . . . . . . . . . . . . . . . 177 6.5.3 Optimization Control By Graphical User Interface . . . . 180 6.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 7 Conclusions 185 A Appendix of Designs 189 Bibliography 201 Index 211