Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book

Optimizing energy-efficiency for multi-core packet processing systems in a compiler framework

Network applications become increasingly computation-intensive and the amount of traffic soars unprecedentedly nowadays. Multi-core and multi-threaded techniques are thus widely employed in packet processing system to meet the changing requirement. However, the processing power cannot be fully utilized without a suitable programming environment. The compilation procedure is decisive for the quality of the code. It can largely determine the overall system performance in terms of packet throughput, individual packet latency, core utilization and energy efficiency. The thesis investigated compilation issues in networking domain first, particularly on energy consumption. And as a cornerstone for any compiler optimizations, a code analysis module for collecting program dependency is presented and incorporated into a compiler framework. With that dependency information, a strategy based on graph bi-partitioning and mapping is proposed to search for an optimal configuration in a parallel-pipeline fashion. The energy-aware extension is specifically effective in enhancing the energy-efficiency of the whole system. Finally, a generic evaluation framework for simulating the performance and energy consumption of a packet processing system is given. It accepts flexible architectural configuration and is capable of performingarbitrary code mapping. The simulation time is extremely short compared to full-fledged simulators. A set of our optimization results is gathered using the framework

Graph Pattern Matching on Symmetric Multiprocessor Systems

Graph-structured data can be found in nearly every aspect of today's world, be it road networks, social networks or the internet itself. From a processing perspective, finding comprehensive patterns in graph-structured data is a core processing primitive in a variety of applications, such as fraud detection, biological engineering or social graph analytics. On the hardware side, multiprocessor systems, that consist of multiple processors in a single scale-up server, are the next important wave on top of multi-core systems. In particular, symmetric multiprocessor systems (SMP) are characterized by the fact, that each processor has the same architecture, e.g. every processor is a multi-core and all multiprocessors share a common and huge main memory space. Moreover, large SMPs will feature a non-uniform memory access (NUMA), whose impact on the design of efficient data processing concepts should not be neglected. The efficient usage of SMP systems, that still increase in size, is an interesting and ongoing research topic. Current state-of-the-art architectural design principles provide different and in parts disjunct suggestions on which data should be partitioned and or how intra-process communication should be realized. In this thesis, we propose a new synthesis of four of the most well-known principles Shared Everything, Partition Serial Execution, Data Oriented Architecture and Delegation, to create the NORAD architecture, which stands for NUMA-aware DORA with Delegation. We built our research prototype called NeMeSys on top of the NORAD architecture to fully exploit the provided hardware capacities of SMPs for graph pattern matching. Being an in-memory engine, NeMeSys allows for online data ingestion as well as online query generation and processing through a terminal based user interface. Storing a graph on a NUMA system inherently requires data partitioning to cope with the mentioned NUMA effect. Hence, we need to dissect the graph into a disjunct set of partitions, which can then be stored on the individual memory domains. This thesis analyzes the capabilites of the NORAD architecture, to perform scalable graph pattern matching on SMP systems. To increase the systems performance, we further develop, integrate and evaluate suitable optimization techniques. That is, we investigate the influence of the inherent data partitioning, the interplay of messaging with and without sufficient locality information and the actual partition placement on any NUMA socket in the system. To underline the applicability of our approach, we evaluate NeMeSys against synthetic datasets and perform an end-to-end evaluation of the whole system stack on the real world knowledge graph of Wikidata

Application of computational intelligence to explore and analyze system architecture and design alternatives

Systems Engineering involves the development or improvement of a system or process from effective need to a final value-added solution. Rapid advances in technology have led to development of sophisticated and complex sensor-enabled, remote, and highly networked cyber-technical systems. These complex modern systems present several challenges for systems engineers including: increased complexity associated with integration and emergent behavior, multiple and competing design metrics, and an expansive design parameter solution space. This research extends the existing knowledge base on multi-objective system design through the creation of a framework to explore and analyze system design alternatives employing computational intelligence. The first research contribution is a hybrid fuzzy-EA model that facilitates the exploration and analysis of possible SoS configurations. The second contribution is a hybrid neural network-EA in which the EA explores, analyzes, and evolves the neural network architecture and weights. The third contribution is a multi-objective EA that examines potential installation (i.e. system) infrastructure repair strategies. The final contribution is the introduction of a hierarchical multi-objective evolutionary algorithm (MOEA) framework with a feedback mechanism to evolve and simultaneously evaluate competing subsystem and system level performance objectives. Systems architects and engineers can utilize the frameworks and approaches developed in this research to more efficiently explore and analyze complex system design alternatives --Abstract, page iv