Recent advances in the field of computational intelligence have led to a numberof promising optimization algorithms. These algorithms have the potential to findoptimal or near-optimal solutions to complex problems within a reasonable timeframe. Structural optimization is a research field where such algorithms are appliedto optimally design structures. Although a significant amount of research has been published in the field ofstructural optimization since the 1960s, little of the research effort has been utilizedin structural design practice. One reason for this is that only a small portion ofthe research targets real-world applications. Therefore there is a need to conductresearch on cost optimization of realistic structures, particularly large structureswhere significant cost savings may be possible. To address this need, a software application for cost optimization of beam bridgeswas developed. The software application was limited to road bridges in concretethat are straight and has a constant width of the bridge deck. Several simplificationswere also made to limit the scope of the thesis. For instance, a rough design ofthe substructure was implemented, and the design of some structural parts wereneglected. This thesis introduces the subject of cost optimization, treats fundamentaloptimization theory, explains how the software application works, and presents acase study that was carried out to evaluate the application. The result of the case study suggests a potential for significant cost savings. Yet,the speeding up of the design process is perhaps the major benefit that should inclinedesigners to favor optimization. These findings mean that current optimizationalgorithms are robust enough to decrease the cost of beam bridges compared to aconventional design. However, the software application needs several improvementsbefore it can be used in a real design situation, which is a topic for future research. / Nya framsteg inom forskningen har lett till ett antal lovande optimeringsalgoritmer.Dessa algoritmer har potentialen att hitta optimala eller nästan optimala lösningartill komplexa problem inom rimlig tid. Strukturoptimering är ett forskningsområdedär dessa algoritmer tillämpas för att dimensionera konstruktioner på ett optimaltsätt. Även om en betydande mängd forskning har publicerats inom området strukturoptimeringsedan 1960-talet, så har endast lite av forskningsinsatserna kommit tillanvändning i praktiken. Ett skäl till detta är att endast en liten del av forskningenär inriktad mot verklighetsförankrade tillämpningar. Därför finns det ett behov avatt bedriva forskning på kostnadsoptimering av realistiska konstruktioner, särskiltstora konstruktioner där betydande kostnadsbesparingar kan vara möjligt. För att möta detta behov har ett datorprogram för kostnadsoptimering avbalkbroar utvecklats. Programmet begränsades till vägbroar i betong som är rakaoch har en konstant bredd. Flera förenklingar gjordes också för att begränsaomfattningen av arbetet. Till exempel implementerades en grov dimensionering avunderbyggnaden, och dimensioneringen av vissa komponenter försummades helt ochhållet. Detta examensarbete presenterar ämnet kostnadsoptimering, behandlar grundläggandeoptimeringsteori, förklarar hur programmet fungerar, och presenterar enfallstudie som genomfördes för att utvärdera programmet. Resultatet av fallstudien visar en potential för betydande kostnadsbesparingar.Trots det så är tidsbesparingarna i dimensioneringsprocessen kanske den störstafördelen som borde locka konstruktörer att använda optimering. Dessa upptäckterinnebär att aktuella optimeringsalgoritmer är tillräckligt robusta för att minskakostnaden för balkbroar jämfört med en konventionell dimensionering. Dock måsteprogrammet förbättras på flera punkter innan det kan användas i en verklig dimensioneringssituation,vilket är ett ämne för framtida forskning.

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Steel is an important engineering material used in a variety of applications due to its mechanical properties and durability. With increasing demand for higher performance, complex structures, and the need for cost reduction within manufacturing processes, there are numerous challenges with traditional steel design options and production methods with manufacturing cost being the most significant. In this research, this challenge is addressed by developing a micro-genetic algorithm to minimize the manufacturing cost while designing steel with the desired mechanical properties. The algorithm was integrated with machine learning models to predict the mechanical properties and microstructure for the generated alloys based on their chemical compositions and heat treatment conditions. Through this, it was demonstrated that new steel alloys with specific mechanical property targets could be generated at an optimal cost. The research’s contribution lies in the development of a different approach to optimize steel production that combines the advantages of machine learning and evolutionary algorithms while increasing the number of input parameters. Additionally, it uses a small dataset illustrating that it can be used in applications where data is lacking. This approach has significant implications for the steel industry as it provides a more efficient way to design and produce new steel alloys. It also contributes to the overall material science field by demonstrating its ability in a material’s design and optimization. Overall, the proposed framework highlights the potential of utilizing machine learning and evolutionary algorithms in material design and optimization. / Thesis / Master of Applied Science (MASc) / This research aims to develop an AI-based functional integrated with a heuristic algorithm that optimizes parameters to meet desired mechanical properties and cost for steels. The developed algorithm generates new alloys which meet desired mechanical property targets by considering alloy composition and heat treatment condition inputs. Used in combination with machine learning models for the mechanical property and microstructure prediction of new alloys, the algorithm successfully demonstrates its ability to meet specified targets while achieving cost savings. The approach presented has significant implications for the steel industry as it offers a quick method of optimizing steel production, which can reduce overall costs and improve efficiency. The integration of machine learning within the algorithm offers a different way of designing new steel alloys which has the potential to improve manufactured products by ultimately improving their performance and quality.

Climate change (CC) science has made significant progress in development of predictive models. Despite these recent advances, the assessment of CC impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Urban watersheds have been the focus of considerable efforts to restore hydrology and water quality, and the aquatic habitat of receiving waters, yet CC impacts threaten to reduce the effectiveness of these efforts. Thus, assessing the impacts of CC in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. Simulations of CC for the 2041-2068 period were developed using downscaled Global Climate Models (GCMs) from the North American Regional CC Assessment Program (NARCCAP) and Coupled Model Intercomparison Project Phase 5 (CMIP5) to forecast precipitation and temperature time series. This data were then used to force a Storm Water Management Model (SWMM) of the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. NARCCAP uses a scenario represents a medium-high greenhouse gas emissions assumption, A2; the latter, uses five GCMs, and two Representative Concentration Pathways (RCP 4.5 and 8.5) scenarios in an ensemble approach to better assess variability of model predictions in presenting precipitation, temperature, runoff quantity and quality. Then, the effects of CC on runoff peak, volume, and nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment performance of a very common stormwater control measure (SCM), retention ponds, was assessed. Rainwater Harvesting (RWH) systems are an unusual SCM in that they recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of RWH systems for projected CC for these dual purposes was assessed. NARCAAP data for selected locations across the U.S. were statistically downscaled using a modified version of the equiratio cumulative distribution function matching method to create a time series of projected precipitation and temperature. These data were used to force a simulation model, the Rainwater Analysis and Simulation Program (RASP) to assess the impacts of CC on RWH with respect to the reliability of water supply and runoff capture. To support CC modeling, an easy-to-use software tool, RSWMM-Cost, was developed. RSWMM-Cost automates the execution of SWMM, which is commonly used for simulating urban watersheds. Several features were incorporated into the RSWMM-Cost tool, including automated calibration, sensitivity analysis, and cost optimization modules; the latter can assist in identifying the most cost-effective combination of SCMs in an urban watershed. As an example, RSWMM-Cost was applied to a headwater subcatchment the Difficult Run watershed. / Ph. D. / Urban watersheds have been the focus of considerable efforts to restore water quantity and quality, and the aquatic habitat of receiving waters, yet climate change impacts threaten to reduce the effectiveness of these efforts. The assessment of climate change impacts in urban watersheds remains an area of active research, in part due to the small temporal and spatial scales needed to adequately characterize urban systems. Thus, assessing the impacts of climate change in urban watershed assessment are essential for assuring the success of water quality improvement programs and is an important research need. In this study, simulations of climate change for the 2041-2068 period were developed to forecast precipitation and temperature data. These data were then used to force a hydrologic model for the Difficult Run watershed of Fairfax County, Virginia, a tributary of Potomac River, which flows into Chesapeake Bay. Then, the effects of climate change on runoff, nutrient and sediment loads delivered to the Chesapeake Bay and on the treatment efficiency of a very common management practice called retention ponds, was assessed. Rainwater harvesting systems are an unusual management practice that recycle and reuse stormwater, normally from rooftops, and increase water supply and reduce runoff. The efficiency of rainwater harvesting systems for projected climate change with respect to the reliability of water supply and runoff capture was assessed for the 2041-2068 period. To support climate change modeling, an easy-to-use tool, was also developed to select the most cost-optimized combination of best management practices in urban watersheds considering site constraints, limitations, and size. As an example, the tool was applied to a headwater subcatchment of the Difficult Run watershed. The ability to assess the impact of climate change on both hydrologic and water quality treatment could assist in the selection of the most appropriate management practices to address water management goals and conserve limited financial resources.

The thesis is concerned with a question, what plays the key role in decision making process about restructuring of production capacities of automotive companies within global process. All of this is running in the background of optimization factors influencing their cost items value. Volkswagen AG, more precisely SKODA AUTO was chosen for behavior analysis introduced as a case study. The goal is to suggest some recommendations for SKODA AUTO to help with achieving sales of 1,5 million cars in 2018 based on global pressures analysis affecting automotive industry and future cost development study in this field. The main benefit of the thesis is the complex cost items analysis resulting in application of the conclusions into SKODA AUTO case in a form of recommendations based on cost reduction.

This diploma thesis deals with spot energy trading in the Czech Republic. The first section focuses on legal market environments, market members, and types of individual markets. This serves as an introduction to the second and the final section, which answers the question of the advantageousness of spot energy trading. The final section illustrates a practical demonstration of an optimization analysis of energy costs in a particular company. The contribution of this thesis, in my opinion, is the practical description of the calculation of energy costs in one company or an association of companies, which are considering the spot energy purchase, and a transfer of the responsibility for the deviation.

I dagens tillverkningsindustri ställs det allt högre krav på produkter av hög kvalité med lägsta möjliga produktionskostnad. För att som producerande industri kunna tillmötesgå dessa krav krävs det att produktionsprocessen optimeras och därigenom sänker kostnaderna. Samtidigt ställs det allt högre krav på tillverkningsindustrin från samhället, en minimal materialförbrukning gentemot förbrukningskraven leder till en ökad hållbar utveckling. För att kunna uppnå visionen av en optimerad produktionsprocess blir kvalitetskontroll idag allt viktigare för industrierna. Målet är att uppnå en produkt av hög kvalité, fri från betydelsefulla variationer och därigenom tillgodose kundens behov eller än hellre överträffa kundens förväntningar på produkten, samtidigt som tillverkningsprocessen är optimerad till små marginaler. Syftet med det här arbetet är därför att bidra med en ökad kunskap om kvalitetskontroll inom produktionsprocessen och några av de vanligaste mätmetoder som används idag. Samt undersöka om en ökad kunskap kan möjliggöra för en optimering av produktionsprocessen genom en produktion med minskade marginaler. Arbetet utförs med en förhoppning om att denna optimering sedan kan leda till en rad fördelar för både den tillverkande industrin men även för samhället, dels genom en reducerad materialanvändning och därigenom en kostnadsoptimering, men även en produkt med optimerade dimensioner som därigenom kan tillverkas effektivare och höja processens produktivitet. Examensarbetet är upplagt som en fallstudie i samarbete med NKT HVC. Mätdata från tillverkningsprocessen har studerats för att upptäcka variationer och samband mellan de olika mätmetoderna. Två produktionslinjer har beaktats i studien. All mätdata från de båda linjerna behandlar en likvärdig produkt.  Resultatet av den empiriska studien indikerar på att det finns möjligheter för en optimering inom produktionsprocessen då det finns skillnader mellan kvalitetskontroll utfört med olika mätinstrument. En ökad kunskap är därför väsentligt för att kunna säkerhetsställa vilken mätmetod som stämmer bäst överens med verkligheten och därigenom kunna optimera processen med en reducerad materialförbrukning och därigenom en kostnadsoptimering samt en ökad produktivitet som resultat. / Today's manufacturing industry places increasingly high demands on high quality products with the lowest possible production cost. To be able to meet these requirements in production, it is necessary to optimize the production process and thereby lowering the costs. At the same time, increasing demands are being made on the manufacturing industry from the community, a minimal material consumption versus the consumption demands leads to sustainable development. In order to achieve the vision of an optimized production process, quality control today becomes increasingly important for the industries. The goal is to achieve a high-quality product, free from significant variations, and thereby meeting the customer needs or even exceeding the customer´s expectations of the product, while at the same time optimizing the manufacturing process to small margins. The purpose of this work is therefore to contribute to increased knowledge of quality control in the production process and some of the most common measurement methods used today. As well as investigating whether increased knowledge can enable an optimization of the production process through production with reduced margins. The work is carried out with the hope that this optimization can lead to several advantages for both the manufacturing industry but also for society, partly through reduced material usage and thus cost optimization, but also a product with optimized dimensions that can thereby be made more efficient and increase the productivity of the process. The Master thesis is done as a case study in cooperation with NKT HVC. Measurement data from the manufacturing process have been studied to detect variations and relationships between the different measurement methods. Two production lines have been considered in the study. All measurement data from the two lines treats an equivalent product. The result of the empirical study indicates that there are opportunities for optimization in the production process as there are differences between quality control performed with different measuring instruments. An increased knowledge is therefore essential in order to ensure which measurement method that fits best with reality, and thus be able to optimize the process with a reduced material consumption and thereby a cost optimization and increased productivity as a result.

Adoption of electric vehicles has been restrained by the availability of charging stations and consumer fear of being stranded with a depleted battery, far from the nearest charger. In many areas of the world, charging stations are now widely available and the transition from vehicles with internal combustion engines is accelerating, though still in a fairly early stage. For electric vehicle drivers in those areas, anxiety that they will not be able to find a charger (“range anxiety”) is subsiding. However, differences in charging speed and pricing between stations and different outlets at the same station can be large. Total trip duration can vary significantly based on the charging outlet selected. Prior research has developed methods for helping all drivers find the fastest route and for electric vehicle drivers to ensure that they are able to complete their trip. Additional research has explored other complexities of route selection for electric vehicles such as how to select optimal stations for charging based on the total trip duration, including driving and charging time. Pricing for recharging electric vehicles at public chargers is more complex and diverse than for gas filling stations due to the differences in charging rates and the relatively low competition. This research investigates those differences. Using design science research methodology, a method is presented for determining which charging stops result in the lowest possible charging cost for a given route. The method is demonstrated through experiment with random routes within Sweden. The experimental results show that the average cost savings as compared to the duration-optimal route is 15% and 139 SEK per additional hour of trip time. One possible direction for future work is to improve the performance of the algorithm for use in real-time consumer route planning applications.

Today in most design offices the practice when designing infrastructure is to adopt a trial-and-error based approach. This means that one solution to the problem is evaluated and based on the results the design is altered. This process is then repeated until satisfactory results have been achieved. This is an iterative process and in this project, a software has been developed as a tool for the practicing engineer to use for preliminary design. The software carries out this iterative process and optimizes the solution using algorithms. The software is developed to evaluate road traffic composite bridges according to the Eurocodes. The bridges are made up from two main steel beams with a concrete deck on top. The software has then been used in a number of different studies in order to evaluate its potential as a preliminary design method. The results show that this is a viable tool for preliminary designs, with the drawback that, each iteration is computationally expensive and therefore, optimization of a real-case problem takes a lot of time in order to find the optimal design. / I dagsläget använder de flesta konstruktionsföretagen sig av en iterativ process när de konstruerar infrastruktur. Man utvärderar en lösning och beroende av resultatet så justeras lösningen och utvärderas på nytt, detta återupprepas tills en tillfredsställande lösning har erhållits. Detta är en iterativ process och i samband med detta arbete så har en mjukvara utvecklats som ett verktyg till konstruktören. Mjukvaran utför det iterativa arbetet och optimerar konstruktionen med hjälp av optimeringsalgoritmer. Programmet utvärderar vägbroar av en samverkanskonstruktion i form av två stålbalkar med en betongplatta ovanpå, beräkningarna sker enligt Eurokoderna. Programmet har använts för att utvärdera det här arbetssättets potential vid anbudsskede i projekt. Resultaten visar att det finns potential för detta med nackdelen att varje iteration är tidskrävande vilket gör att optimering av ett realistiskt projekt tar lång tid.

The United Nations’ Sustainable Development Goal 7 (SDG7) of Agenda 2030 calls for an increase in the use of renewable energy sources, among other targets. The percentage of fossil fuel-fired thermal generation for electricity is increasingly being reduced as renewable energy technologies (RET) advance in cost-competitiveness, and as greenhouse gas and industrial air pollutant emission limits become more stringent. In certain cases, renewable energy contributes to energy security by improving a nation’s trade balance, since local resources are harnessed and imports are reduced. RET investments are becoming more frequent gaining a sizeable share in the electric power mix of numerous countries. However, RET is affected by existing fossil fuel-fired electricity generation, especially in countries that have domestic reserves. While coal may be dirty, others such as natural gas provide multiple benefits, presenting a challenge to renewables. Additionally, RET endowment varies for each geographical location. This often does not correspond to the location of major electricity demand centers.  Therefore, large scale RET adoption and integration becomes logistically more cumbersome, as it necessitates existence of a developed grid network. Utilizing a series of analyses in two different settings – Africa and Cyprus – this thesis draws insights on RET growth policy and the level of technology representation in long term energy models. In order to capture specific challenges of RET integration, enhancements in traditional long-term energy system models are called for and carried out.  The case of Africa is used to assess adoption of RET under various trade scenarios. It is home to some of the world’s greatest RET resource potential and the single largest potential RET project, Grand Inga.  While, the island of Cyprus has goals of introducing large percentages of RET into its electric power mix. Each have important idiosyncrasies which are reflected in the analysis. On the one hand, natural gas competes with RET in Cyprus and forms a key transition fuel away from oil. On the other hand, lack of cross-border interconnectors limit RET project development across Africa. / <p>QC 20170519</p>