Co-Simulation of distributed flexibility coordination schemes

Cílem práce je implementovat a otestovat simulační prostředí, které umožní spojení simulátorů různých typů úloh. Toto prostředí je aplikováno na simulaci koordinovaného optimálního řízení spotřeby energie 20 domácností s různými požadavky na velikost spotřeby a možnostmi uložení energie. Výsledky ukazují, že koordinované řízení spotřeby energie více domácností může dosáhnout značných úspor ve srovnání s řízením spotřeby jednotlivých domácností bez ohledu na ostatní.The goal of the thesis is to implement and test co-simulation environment making it possible to connect simulators of different type. The environment is applied on simulation of coordinated optimal control of energy consumption of 20 households with different preferences on energy supply and its storage capacity. The results show that coordinated control of energy consumption may achieve considerable savings in comparison with control of individual households regardless to the others

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Energy and performance-optimized scheduling of tasks in distributed cloud and edge computing systems

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival and heterogeneity of tasks. This dissertation proposes a class of energy and performance-optimized scheduling algorithms built on top of several intelligent optimization algorithms. This dissertation includes two parts, including background work, i.e., Chapters 3–6, and new contributions, i.e., Chapters 7–11. 1) Background work of this dissertation. Chapter 3 proposes a spatial task scheduling and resource optimization method to minimize the total cost of CDCs where bandwidth prices of Internet service providers, power grid prices, and renewable energy all vary with locations. Chapter 4 presents a geography-aware task scheduling approach by considering spatial variations in CDCs to maximize the profit of their providers by intelligently scheduling tasks. Chapter 5 presents a spatio-temporal task scheduling algorithm to minimize energy cost by scheduling heterogeneous tasks among CDCs while meeting their delay constraints. Chapter 6 gives a temporal scheduling algorithm considering temporal variations of revenue, electricity prices, green energy and prices of public clouds. 2) Contributions of this dissertation. Chapter 7 proposes a multi-objective optimization method for CDCs to maximize their profit, and minimize the average loss possibility of tasks by determining task allocation among Internet service providers, and task service rates of each CDC. A simulated annealing-based bi-objective differential evolution algorithm is proposed to obtain an approximate Pareto optimal set. A knee solution is selected to schedule tasks in a high-profit and high-quality-of-service way. Chapter 8 formulates a bi-objective constrained optimization problem, and designs a novel optimization method to cope with energy cost reduction and QoS improvement. It jointly minimizes both energy cost of CDCs, and average response time of all tasks by intelligently allocating tasks among CDCs and changing task service rate of each CDC. Chapter 9 formulates a constrained bi-objective optimization problem for joint optimization of revenue and energy cost of CDCs. It is solved with an improved multi-objective evolutionary algorithm based on decomposition. It determines a high-quality trade-off between revenue maximization and energy cost minimization by considering CDCs’ spatial differences in energy cost while meeting tasks’ delay constraints. Chapter 10 proposes a simulated annealing-based bees algorithm to find a close-to-optimal solution. Then, a fine-grained spatial task scheduling algorithm is designed to minimize energy cost of CDCs by allocating tasks among multiple green clouds, and specifies running speeds of their servers. Chapter 11 proposes a profit-maximized collaborative computation offloading and resource allocation algorithm to maximize the profit of systems and guarantee that response time limits of tasks are met in cloud-edge computing systems. A single-objective constrained optimization problem is solved by a proposed simulated annealing-based migrating birds optimization. This dissertation evaluates these algorithms, models and software with real-life data and proves that they improve scheduling precision and cost-effectiveness of distributed cloud and edge computing systems

Cognitive Robotic Disassembly Sequencing For Electromechanical End-Of-Life Products Via Decision-Maker-Centered Heuristic Optimization Algorithm

End-of-life (EOL) disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and models to solve related problems. End-of-life disassembly focuses on regaining the value added into products which are considered to have completed their useful lives due to a variety of reasons such as lack of technical functionality and/or lack of demand. Disassembly is known to possess unique characteristics due to possible changes in the EOL product structure and hence, cannot be considered as the reverse of assembly operations. With the same logic, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence need to be regenerated to accommodate these unforeseeable changes. That is, if one or more components which were included in the original bill-of-material (BOM) of the product is missing and/or if one or more joint types are different than the ones that are listed in the original BOM, the sequencer needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by highly adaptive methodologies justifying the utilization of image detection technologies for online real-time disassembly. These methodologies should also be capable of handling efficient search techniques which would provide equally reliable but faster solutions compared to their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristics techniques such as Genetic Algorithm (GA), Tabu Search (TS), Ant Colony Optimization (ACO), Simulated Annealing (SA) and Neural Networks (NN). As with any data driven technique, the performance of the proposed methodologies is heavily reliant on the accuracy and the flexibility of the algorithms and their abilities to accommodate several special considerations such as preserving the precedence relationships during disassembly while obtaining near-optimal or optimal solutions. This research proposes three approaches to the EOL disassembly sequencing problem. The first approach builds on previous disassembly sequencing research and proposes a Tabu Search based methodology to solve the problem. The objectives of this proposed algorithm are to minimize: (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the results of the previous Genetic Algorithm with the proposed Tabu Search Algorithm is also included. Following this, the disassembly sequencing problem is further investigated by introducing an automated disassembly framework for end-of-life electronic products. This proposed model is able to incorporate decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. The proposed disassembly sequencing approach is composed of two steps. The first step involves the detection of objects and deals with the identification of precedence relationships among components. This stage utilizes the BOMs of the EOL products as the primary data source. The second step identifies the most appropriate disassembly operation alternative for each component. This is often a challenging task requiring expert opinion since the decision is based on several factors such as the purpose of disassembly, the disassembly method to be used, and the component availability in the product. Given that there are several factors to be considered, the problem is modeled using a multi-criteria decision making (MCDM) method. In this regard, an Analytic Hierarchy Process (AHP) model is created to incorporate DMs’ verbal expressions into the decision problem while validating the consistency of findings. These results are then fed into a metaheuristic algorithm to obtain the optimum or near-optimum disassembly sequence. In this step, a metaheuristic technique, Simulated Annealing (SA) algorithm, is used. In order to test the robustness of the proposed Simulated Annealing algorithm an experiment is designed using an Orthogonal Array (OA) and a comparison with an exhaustive search is conducted. In addition to testing the robustness of SA, a third approach is simultaneously proposed to include multiple stations using task allocation. Task allocation is utilized to find the optimum or near-optimum solution to distribute the tasks over all the available stations using SA. The research concludes with proposing a serverless architecture to solve the resource allocation problem. The architecture also supports non-conventional solutions and machine learning which aligns with the problems investigated in this research. Numerical examples are provided to demonstrate the functionality of the proposed approaches