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

Approved for public release, distribution is unlimited / This thesis develops an integer linear program called MSAMT (Model to Schedule Army Maneuver Training) to schedule an Army installation's maneuver training. We demonstrate MSAMT using a data set containing 261 platoon-level, 67 company-level and 18 battalion-level units, and 7 major training areas located at Fort Hood, Texas. Using a typical near-term planning horizon from 6 to 8 weeks, MSAMT schedules daily training for a randomly selected set of the stationed units and training requirements. For a 6-week time period and almost 65% (63 platoons 16 companies and 5 battalions) of the units there are 151 platoon-level, 51 company-level and 11 battalion-level required tasks of which MSAMT can schedule 93%. When the subset of units is increased to 80% (75 platoons, 20 companies, 6 battalions), there are 187 platoon-level, 62 company-level and 11 battalion-level tasks of which MSAMT can schedule only 85%. Maintaining the 80% unit level but having an 8 weekperiod increases required training achieved to 94%. Such results can help determine the ability of an Army installation to satisfy training requirements of its stationed units as well as identify a shortage or excess in available training land. It can show the training impact of changing the quantity of units at an installation and thereby aid in base realignment and closure decisions. / First Lieutenant, Turkish Army

Title: Project portfolio optimization with time and resources Author: Bc. Tomáš Huml Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor: Doc. RNDr. Roman Barták, Ph.D Abstract: Traditional project portfolio optimization deals with static projects that are not evolving in time. The focus of this diploma thesis is on projects that are spread in time, typically such projects consists of a sequence (or other partially ordered structure) of actions that require some resources (money, people, etc.) for realization. Then the project portfolio optimization deals with selecting a subset of projects according to given time and space (resource) restrictions and optimizing certain criteria such as overall profit. This problem is very close to oversubscribed scheduling where the most profitable subset of orders is being scheduled. Hence scheduling techniques will be the main inspiration for solving this new type of problems. Lots of modelling algorithms for optimal portfolio selection are proposed in this diploma thesis and several of them are implemented in a program which is part of this thesis as well. Keywords: portfolio optimization, integer linear programming (ILP), workflow optimization, project interdependencies

[pt] No Brasil, as três principais fontes renováveis de energia elétrica são eólica, pequenas centrais hidrelétricas (PCHs) e biomassa. A comercialização da energia proveniente dessas fontes ocorre majoritariamente no ambiente de contratação regulada (ACR), através de leilões, em detrimento do ambiente de contratação livre (ACL). Isso devido ao fato de seus recursos naturais serem sazonais, estabelecendo o risco de preço-quantidade no ACL, em que o excesso ou déficit de energia gerada em relação à quantidade contratada é liquidado ao preço de liquidação de diferenças (PLD), uma variável sistêmica e altamente volátil. Contudo, a complementaridade dessas fontes permite reduzir esses riscos quando a energia é comercializada de forma conjunta, através de um fundo de energia que gera aumento do valor do portfólio com relação à comercialização individual. Esta dissertação utiliza a teoria de jogos cooperativos para analisar formas de repartir o benefício gerado, através da alocação de quotas financeiras. O conjunto de soluções onde o resultado individual das fontes no fundo é maior do que o resultado individual em qualquer subcoalisão define o núcleo do jogo. Assim, a complexidade de encontrar uma solução dentro do núcleo depende do número de subcoalizões, que cresce exponencialmente com o número de jogadores. Nesse contexto, este trabalho se propôs a apresentar: (i) um modelo de portfólio que incentiva a participação de fontes renováveis no ACL; (ii) um modelo de programação linear que busca o núcleo do jogo; (iii) uma metodologia eficiente baseada em decomposição de Benders, capaz de suprimir a questão da explosão combinatória do problema. / [en] In Brazil, the three main sources of renewable energy are wind, small run-of-river hidros (SH) and biomass. The energy sale of such sources occurs mainly in the Regulated Trading Environment (RTE), through auctions, with shy occurrences in the Free Trading Environment (FTE). This is due to the fact that their natural resources are seasonal, establishing the so-called price-quantity risk in the FTE, as the surplus or deficit of energy generated relative to the contracted amount is settled at the market’s spot price, a systemic and highly volatile variable. However, the complementary nature of these sources allows risk reduction if their energy are trade jointly, through an energy hedge pool that increases the value of the portfolio in comparison to individual strategies. This work makes use of cooperative games theory to analyze ways of sharing the generated benefit, through financial quotas allocation. The set of solutions where the individual sources results in the pool are greater than its results at any possible subcoalition defines the core of the game. Thus, the challenge of finding a solution inside the core depends on the number of subcoalitions, which grows exponentially with the number of players. In this context, this work proposes to present: (i) a model of portfolio that encourages the penetration of renewable sources in the FTE; (ii) a linear programming model that pursuits the game’s core; (iii) an efficient methodology based on Benders decomposition that is capable of suppress the problem of combinatorial explosion, typical of cooperative games with many players.