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Optical networks employing wavelength division multiplexing technology have been well recognized as the core networks for the next generation Internet. In such networks, any fiber cut or node failure may lead to huge data loss. Thus, reliability is of great importance in the design of modern high-speed networks. At the same time, traffic grooming is another important design objective since it addresses multi-granularity traffic. The traditional routing approaches with differentiated services do not consider the traffic grooming case or reconfiguration method. Therefore, they are not resource-efficient for the next generation Internet. In this dissertation, an effective reconfigurable traffic grooming with differentiated reliability scheme is proposed to efficiently use network resources. Compared with the conventional rerouting method, the proposed scheme makes the network more robust and immune from service interruptions. An integer linear programming (ILP) formulation is presented first. By solving the ILP formulation, an optimal solution is obtained for each incoming connection request. However, the solution is so time consuming, a heuristic algorithm is introduced to get an approximate optimal solution. The performance evaluation indicates that the connection blocking probability can be decreased greatly by the proposed scheme.

The scheduling of the converter aisle of a nickel smelting plant is a non-trivial task with significant consequences to plant profitability and production. An optimization-based scheduling formulation is developed using a continuous-time paradigm to accurately represent event timings. The formulation accounts for environmental restrictions on sulfur dioxide emissions using event timing constraints. The formulation includes novel semi-continuous modeling to represent flash furnaces which operate with a continuous inlet flow and intermittent discrete material removal, as well as, a novel sequencing and symmetry-breaking scheme to account for identical units operating in parallel. A rolling horizon feature is included in the formulation to accommodate multi-period optimization. Tightening constraints are developed and used to improve the computational performance of the optimization and demonstrate the capacity of the proposed methodology to function as a real-time decision-support tool. A solution procedure is presented where an aggregate model is used to bound the objective function of the master problem in a two layer optimization scheme. Finally, a novel multi-tiered procedure is presented to enhance the optimization solution by re-optimizing for objectives of decreasing priority in order to minimize task start times and penalize deviations in the furnace flow rate. To address the closed-loop properties of scheduling, a reactive scheduling mechanism is included to allow for rescheduling to account the impact of process disturbances on the operating schedule. A methodology for reducing radical scheduling changes due to the optimization during reactive scheduling is presented. The reactive scheduling algorithm utilizes a tiered optimization approach that progressively increases the degrees of freedom available, as required, in order to achieve a feasible production schedule. The use of the reactive scheduling algorithm demonstrates the ability to reject disturbances and transition plant operation in an agile manner. / Thesis / Master of Applied Science (MASc)

Localized heating leads to generation of thermal Hotspots that affect performance and reliability of an Integrated Circuit(IC). Functional workloads determine the locations and temperature of hotspots on a die. Programs are classified into phases based on program execution profile. During a phase, spatial power dissipation pattern of an application remains unchanged. In this thesis, we present a systematic approach for developing a synthetic workload from a functional workload to create worst case temperature of a target hotspot in 2D and 3D IC. These synthetic workload are designed to create thermal stress patterns, which would help in characterizing the thermal characteristics of micro architecture to worst case temperature transient which is an important problem in Industry. Our approach is based on the observation that, worst case temperature at a particular location in 2 D IC is determined not only by the current activity in that region, but also by the past activities in the surrounding regions. Therefore, if the surrounding areas were “pre-heated” with a different workload, then the target region may become hotter due to slower rate of lateral heat dissipation Similarly in case of 3D IC, the workload applied to each of the dies in 3D IC keeps on changing continuously, thus the hotspot could be found in any of the stacked layers. Thus the creation of localized hotspot at a particular location in a stacked 3D IC layer depends not only on the present activity at that location but also on the previous activity in the surrounding region and also on the activity of layers below it. Accordingly, (i) we develop a wavelet-based canonical spatio-temporal heat dissipation model for program traces, and use (ii) a novel Integer Linear Programming (ILP) formulation to rearrange program phases to generate target worst case hotspot temperature in 2D and 3D IC. We apply this formulation to target another well-known problem of (iii) maximizing temperature between a pair of co-ordinates in an IC. Experimental results show that by taking the spatio-temporal effect into account and with dynamic phase change behavior, we could raise temperature of a hotspot higher than what is possible otherwise. ICs are often tested at worst-case system operating conditions to assure that, all ICs shipped will function properly in the end system. Thus hotspot temperature maximization is an important in design verification and testing.

Electric trucks and platooning technology are expected to be part of the transportation system in the near future. Therefore, it is important to develop platoon coordination strategies and study the potential of platooning for when trucks are electric. In this paper, we study the platoon coordination problem at a single charging station where electric trucks can charge while they wait for other trucks to form platoons with.We assume all trucks to have identical routes after the charging station. The objective is to maximize the total reward of all trucks, including the platooning profit and cost of waiting.Moreover, the trucks have waiting time constraints to respect their mission deadlines and charging time constraints to make sure they can travel between the hub and destination without running out of battery. The energy consumption is decreased when driving as a follower truck in a platoon, which decreases the minimum charging time for the truck. We formulate the platoon coordination problem of electric trucks as a linear integer optimization problem. To evaluate the method, it was compared to a simpler coordination method. The savings from platooning with electric vehicles, using both coordination methods, were also compared to platooning with diesel trucks. The results showed that platooning with electric vehicles can save up to 10% of the driving cost and therefore have significant economic benefits. It was also shown that the method has an acceptable computational efficiency for real-time coordination. / Inom en snar framtid förväntas elektriska lastbilar och platooning vara en del av transportsystemet. Det är därför viktigt att utveckla strategier för platoonkoordinering och undersöka potentialen av platooning med elektriska lastbilar. I det här pappret studerar vi ett platoonkoordineringsproblem med en gemensam startpunkt och en gemensam slutpunkt för alla lastbilar. Startpunkten är en laddningsstation där lastbilarna kan kombinera laddning med att vänta in andra lastbilar att forma platooner med. Lastbilarna i systemet har även samma rutt mellan de två punkterna. Målet är att maximera den totala vinsten för alla lastbilar, med hänsyn till både platooningvinsten och kostnaden för att vänta. Utöver det har lastbilarna begränsad väntetid för att hålla sina deadlines. Vi behöver även ta hänsyn till lastbilarnas laddningstider då de behöver ha tillräckligt med laddning för att åka hela resan från startpunkt till slutdestination. Energikonsumtionen minskar när en lastbil åker som följare vilket minskar den minimala laddningstiden som behövs för att åka hela sträckan. Vi formulerar koordineringsproblemet med elektriska lastbilar som ett linjärt heltalsoptimeringsproblem. För att utvärdera metoden jämfördes den med en enklare koorineringsmetod. Besparingarna från platooning med elektriska lastbilar, med båda koordineringsmetoderna, jämfördes även med platooning med diesellastbilar. Resultatet visade att platooning med ellastbilar kan spara upp till 10% av körkostnaderna och har därför betydande ekonomiska fördelar. Det visades också att metoden har en acceptabel beräkningseffektivitet för koordinering i realtid. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

Additional electricity meters behind the grid access point can improve understanding of energy consumption patterns and thus, adjust consumption behavior. For this study, smart meters were installed in three hostels, out of which two are analyzed further in this paper. Starting from an onsite inspection, all appliances were assigned to reasonable groups for sub-metering. Based on data for the year 2021, the sites are characterized according to the sub-metering concept. In addition, load profiles for type-days are derived, which allows to establish a baseload during COVID lockdown and compare it to consumption patterns for normal occupation. In the prescriptive part, the demand profiles are analyzed regarding their economic potential for load shifting. Consumption data for one week with normal occupation is used as input for techno-economic modeling. The mixed-integer model minimizes electricity purchasing costs for different scenarios including dynamic tariffs and onsite generation from photovoltaics.

This study derives the levelized cost of heat (LCOH) for exemplary post-fossil district heating (DH) scenarios. The DH system of Leipzig in 2040 under the assumption of a completely climate-neutral heat supply is considered. Accordingly, four generation scenarios (GS) are proposed based on different energy carriers that are characterized as follows: (1) natural gas with carbon capture and storage, (2) hydrogen, (3) diversified mix of biomass, waste heat and solar, and (4) electricity. In addition, the scenarios’ robustness toward commodity prices is investigated using a sensitivity analysis. A modeling environment was used to optimize the hourly economic dispatch. Based on this, levelized costs are determined. For the reference case, the LCOH of the GS 1 and 2 exceeds the LCOH of GS 3 and 4. Furthermore, the results indicate that relying on singular energy carriers as opposed to diversified generation portfolios leads to less robust LCOH regarding price sensitivities.

This thesis considers two kinds of problems, motivated by practical applications in data center operations and maintenance. Data centers are the brain of the internet, each hosting as many as tens of thousands of IT devices, making them a considerable global energy consumption contributor (more than 1 percent of global power consumption). There is a large body of work at different layers aimed at reducing the total power consumption for data centers. One of the key places to save power is addressing the thermal heterogeneity in data centers by thermal-aware workload distribution. The corresponding optimization problem is challenging due to its combinatorial nature and the computational complexity of thermal models. In this thesis, a holistic theoretical approach is proposed for thermal-aware workload distribution which uses linearization to make the problem model-independent and easier to study. Two general optimization problems are defined. In the first problem, several cooling parameters and heat recirculation effects are considered, where two red-line temperatures are defined for idle and fully utilized servers to allow the cooling effort to be reduced. The resulting problem is a mixed integer linear programming problem which is solved approximately using a proposed heuristic. Numerical results confirm that the proposed approach outperforms commonly considered baseline algorithms and commercial solvers (MATLAB) and can reduce the power consumption by more than 10 percent. In the next problem, additional operational costs related to reliability of the servers are considered. The resulting problem is solved by a generalization of the proposed heuristics integrated with a Model Predictive Control (MPC) approach, where demand predictions are available. Finally, in the second type of problems, we address a problem in inventory management related to data center maintenance, where we develop an efficient dynamic programming algorithm to solve a lot-sizing problem. The algorithm is based on a key structural property that may be of more general interest, that of a just-in-time ordering policy. / Thesis / Doctor of Philosophy (PhD) / Data centers, each hosting as many as tens of thousands of IT devices, contribute to a considerable portion of energy usage worldwide (more than 1 percent of global power consumption). They also encounter other operational costs mostly related to reliability of devices and maintenance. One of the key places to reduce energy consumption is through addressing the thermal heterogeneity in data centers by thermal-aware work load distribution for the servers. This prevents hot spot generation and addresses the trade-off between IT and cooling power consumption, the two main power consump tion contributors. The corresponding optimization problem is challenging due to its combinatorial nature and the complexity of thermal models. In this thesis, we present a holistic approach for thermal-aware workload distribution in data centers, using lin earization to make the problem model-independent and simpler to study. Two quite general nonlinear optimization problems are defined. The results confirm that the proposed approach completed by a proposed heuristic solves the problems efficiently and with high precision. Finally, we address a problem in inventory management related to data center maintenance, where we develop an efficient algorithm to solve a lot-sizing problem that has a goal of reducing data center operational costs.

With the increase of device complexity and test-data volume required to guarantee adequate defect coverage, external testing is becoming increasingly difficult and expensive. Logic Built-in Self Test (LBIST) is a viable alternative test strategy as it helps reduce dependence on an elaborate external test equipment, enables the application of a large number of random tests, and allows for at-speed testing. The main problem with LBIST is suboptimal fault coverage achievable with random vectors. LFSR reseeding is used to increase the coverage. However, to achieve satisfactory coverage, one often needs a large number of seeds. Computing a small number of seeds for LBIST reseeding still remains a tremendous challenge, since the vectors needed to detect all faults may be spread across the huge LFSR vector space. In this work, we propose new methods to enable the computation of a small number of LFSR seeds to cover all stuck-at faults as a first-order satisfiability problem involving extended theories. We present a technique based on SMT (Satisfiability Modulo Theories) with the theory of bit-vectors to combine the tasks of test-generation and seed computation. We describe a seed reduction flow which is based on the `chaining' of faults instead of pre-computed vectors. We experimentally demonstrate that our method can produce very small sets of seeds for complete stuck-at fault coverage. Additionally, we present methods for inserting test-points to enhance the testability of a circuit in such a way as to allow even further reduction in the number of seeds. / Master of Science

Portfolio optimization is a crucial practice in finance aimed at maximizing the return while minimizing the risk through strategic asset allocation. This paper explores two distinct approaches to modeling robust portfolio optimization, comparing their efficacy in balancing the return and the risk. The first approach focuses on diversifying the portfolio by varying the number of stocks and sector allocation, while the second approach emphasizes minimizing risk by selecting stocks with low correlation. Theoretical foundations and mathematical formulations underpinning these approaches are discussed, incorporating concepts from Modern Portfolio Theory and Mixed Integer Linear Programming. Practical implementation involves data collection from Yahoo Finance API and computational analysis using Python and the optimization tool Gurobi. The results of these methodologies are evaluated, considering factors such as budget constraints, maximum and minimum investment limits, binary constraints, and correlation thresholds. The study concludes by discussing the implications of these findings and their relevance in contemporary financial decision-making processes.