New technology, which has emerged as a part of the new industrial revolution, Industry 4.0, is changing the reality ofmanufacturing companies. Manufacturing companies are perplexed on which technologies to adopt in their productionprocesses, and when is the right time. This can be defined as a company’s ability to prepare for the future, andespecially, for these future technological challenges. A large part of being able to adopt these technologies into theproduction processes is related to the company’s ability to be innovative in their production processes. This is known asprocess innovation. Therefore, this thesis investigates how a production unit within a manufacturing company canmanage process innovation to prepare for future technological challenges. This purpose has been achieved by conducting a single case study, where the case company’s process innovation abilitieswere assessed, and combined with necessary tools to prepare the case company for the future. The case company chosenfor this thesis was Scania. They are a world-leading manufacturer of heavy trucks, buses, and engines. The case studyfocused on one of their productions units, which produce gearboxes and axles. The data for the study was collectedthrough qualitative interviews with members of the case company. The results indicate that the case company needs toimprove their process innovation abilities in order for them to be able to adopt new technologies in their productionprocess. They need a definition of innovation, and a strategy that includes innovation so that it is prioritized andviewed as important. Furthermore, management needs to encourage innovation, and increase their level of risk-taking sothat the culture of the case company is compatible with innovation. Finally, the results show that the case companymay need to redefine or reconsider their fundamental management principle “continuous improvement” to further givepriority to innovation in their production process. Furthermore, an overview of the necessary tools for preparing for thefuture are also covered. These are trend analysis, scenario planning, benchmarking, and technological roadmap. Usingthese four tools can help the company when making strategic decisions about the implementation of new technologies. Finally, this thesis contributes to science by providing empirical research on the topic of process innovation and gives aninteresting perspective on how it can be used to prepare for the future.

This thesis presents the results of basically two separate studies. The first study involved identifying structural and earthquake parameters that influenced seismic structural collapse. The parameter study involved nonlinear dynamic analyses using single-degree-of-freedom (SDOF) bilinear models. Four parameters were associated with the SDOF models — the lateral stiffness, the post-yield stiffness ratio, the yield strength, and the stability ratio (P-Delta effects). Then, three parameters were associated with the ground motions — the records themselves, the lateral ground motion scales, and the vertical ground motion scales. From the parameter study, it was found that the post-yield stiffness ratio augmented by P-Delta effects (rp) in conjunction with the ductility demand was the best predictor of collapse. These two quantities include all four structural parameters and the seismic displacement demands. It was also discovered in the parameter study that vertical accelerations did not significantly influence lateral displacements unless a given combination of model and earthquake parameters was altered such that the model was on the verge of collapsing. The second study involved Incremental Dynamic Analysis (IDA) using bilinear SDOF models representative of low rise buildings in both the Western United States (WUS) and the Central and Eastern United States (CEUS). Models were created that represented three, five, seven, and nine story buildings. Five sites from both the WUS and CEUS were used. Four different damage measures were used to assess the performance of the buildings. The IDA study was primarily interested in the response of the structures between the earthquake intensities that have a 10 percent probability of occurring in 50 years (10/50) and 2 percent probability of occurring in 50 years (2/50). The results showed that all structures could be in danger of severe damage and possible collapse, depending on which damage measure and which earthquake was used. It is important to note, though, that the aforementioned is based on a damage-based collapse rule. The damage-based rule results were highly variable. Using an intensity-based collapse rule, proved to be more consistent. Due to the nature of the bilinear models, only those structures with negative rp values ever collapsed using an intensity-based collapse rule. Most of the WUS models had positive rp values and many of the CEUS models had negative rp values. While many of the CEUS structures had negative rp values, which made them prone to collapse, most of the CEUS structures analyzed did not collapse at the 2/50 intensity. The reason was that the periods of the CEUS models were much longer than the approximate periods that were required to determine the strengths. Consequently, the strength capacity of most of the CEUS models was much greater than the seismic strength demands. While many of the CEUS models did have sudden collapses due to the large negative rp values, the collapses happened at intensities that were generally much higher than the 2/50 event. In the IDA, it was also shown that vertical accelerations can significantly affect the ductility demands of a model with a negative rp post-yield stiffness ratio as the earthquake intensity approaches the collapse intensity. Since IDA is concerned with establishing collapse limit states, it seems that the most accurate collapse assessments would include vertical accelerations. / Master of Science

Random sampling has become a crucial component of modern data management systems. Although the literature on database sampling is large, there has been relatively little work on the problem of maintaining a sample in the presence of arbitrary insertions and deletions to the underlying dataset. Most existing maintenance techniques apply either to the insert-only case or to datasets that do not contain duplicates. In this paper, we provide a scheme that maintains a Bernoulli sample of an underlying multiset in the presence of an arbitrary stream of updates, deletions, and insertions. Importantly, the scheme never needs to access the underlying multiset. Such Bernoulli samples are easy to manipulate, and are well suited to parallel processing environments. Our method can be viewed as an enhancement of the 'counting sample' scheme developed by Gibbons and Matias for estimating the frequency of highly frequent items. We show how the 'tracking counters' used by our maintenance scheme can be exploited to estimate population frequencies, sums, and averages in an unbiased manner, with lower variance than the usual estimators based on a Bernoulli sample. The number of distinct items in the multiset can also be estimated without bias. Finally, we discuss certain problems of subsampling and merging that a rise in systems with limited memory resources or distributed processing, respectively.

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The large increase in on-road vehicles over the years has resulted in cities facing challenges in providing high-quality transportation services. Traffic jams are a clear sign that cities are overwhelmed, and that current transportation networks and systems cannot accommodate the current demand without a change in policy, infrastructure, transportation modes, and commuter mode choice. In response to this problem, cities in a number of countries have started putting a threshold on the number of vehicles on the road by deploying a partial or complete ban on cars in the city center. For example, in Oslo, leaders have decided to completely ban privately-owned cars from its center by the end of 2019, making it the first European city to totally ban cars in the city center. Instead, public transit and cycling will be supported and encouraged in the banned-car zone, and hundreds of parking spaces in the city will be replaced by bike lanes. As a government effort to support bicycling and offer alternative transportation modes, bike-sharing systems (BSSs) have been introduced in over 50 countries. BSSs aim to encourage people to travel via bike by distributing bicycles at stations located across an area of service. Residents and visitors can borrow a bike from any station and then return it to any station near their destination. Bicycles are considered an affordable, easy-to-use, and, healthy transportation mode, and BSSs show significant transportation, environmental, and health benefits. As the use of BSSs have grown, imbalances in the system have become an issue and an obstacle for further growth. Imbalance occurs when bikers cannot drop off or pick-up a bike because the bike station is either full or empty. This problem has been investigated extensively by many researchers and policy makers, and several solutions have been proposed. There are three major ways to address the rebalancing issue: static, dynamic and incentivized. The incentivized approaches make use of the users in the balancing efforts, in which the operating company incentives them to change their destination in favor of keeping the system balanced. The other two approaches: static and dynamic, deal with the movement of bikes between stations either during or at the end of the day to overcome station imbalances. They both assume the location and number of bike stations are fixed and only the bikes can be moved. This is a realistic assumption given that current BSSs have only fixed stations. However, cities are dynamic and their geographical and economic growth affects the distribution of trips and thus constantly changing BSS user behavior. In addition, work-related bike trips cause certain stations to face a high-demand level during weekdays, while these same stations are at a low-demand level on weekends, and thus may be of little use. Moreover, fixed stations fail to accommodate big events such as football games, holidays, or sudden weather changes. This dissertation proposes a new generation of BSSs in which we assume some of the bike stations can be portable. This approach takes advantage of both types of BSSs: dock-based and dock-less. Towards this goal, a BSS optimization framework was developed at both the tactical and operational level. Specifically, the framework consists of two levels: predicting bike counts at stations using fast, online, and incremental learning approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a multi-objective supervised clustering algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, a dynamic, easy-to-interpret, rapid approach to predict bike counts at stations in a BSS was developed. Third, a univariate inventory model using a Markov chain process that provides an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations, using an agent-based simulation approach as a proof-of-concept was developed. Fifth, mathematical and heuristic approaches were proposed to balance bike stations. / Doctor of Philosophy / Large urban areas are often associated with traffic congestion, high carbon mono/dioxide emissions (CO/CO2), fuel waste, and associated decreases in productivity. The estimated loss attributed to missed productivity and wasted fuel increased from $87.2 to $115 between 2007 and 2009. Driving in congested areas also results in long trip times. For instance, in 1993, drivers experienced trips that were 1.2 min/km longer in congested conditions. As a result, commuters are encouraged to leave their cars at home and use public transportation modes instead. However, public transportation modes fails to deliver commuters to their exact destination. Users have to walk some distance, which is commonly called the “last mile”. Bike sharing systems (BSSs) have started to fill this gap, offering a flexible and convenient transportation mode for commuters, around the clock. This is in addition to individual financial savings, health benefits, and reduction in congestion and emissions. Resent reports have shown BSSs multiplying over 50 countries. This notable expansion of BSSs also brings daily logistical challenges due to the imbalanced demand, causing some stations to run empty while others become full. Rebalancing the bike inventory in a BSS is crucial to ensure customer satisfaction and the whole system’s effectiveness. Most of the operating costs are also associated with rebalancing. The current rebalancing approaches assume stations are fixed and thus don’t take into account that the demand changes from weekday to weekend as well as from peak to non-peak hours, making some stations useless during specific days of the week and times of day. Furthermore, cities change continually with regard to demographics or structures and thus the distribution of trips also changes continually, leading to re-installation of stations to accommodate the dynamic change, which is both impractical and costly. In this dissertation, we propose a new generation of BSS in which we assume some stations are portable, meaning they can move during the day. They can be either stand-alone or an extension of existing stations with the goal of accommodating the dynamic changes in the distribution of trips during the day. To implement our new BSSs, we developed a BSS optimization framework. This framework consists of two components: predicting the bike counts at stations using fast approaches and then balancing the system using portable stations. The goal is to propose a framework to solve the dynamic bike sharing repositioning problem, aiming at minimizing the unmet demand, leading to increased user satisfaction and reducing repositioning/rebalancing operations. This dissertation contributes to the field in five ways. First, a novel algorithm was developed to identify the similarity of bike-usage with respect to time events. Second, easy-to-interpret and rapid approaches to predict bike counts at stations in a BSS were developed. Third, an inventory model using statistical techniques that provide an optimal range of bike levels at stations was created. Fourth, an investigation of the advantages of portable bike stations was developed. Fifth, mathematical approach was proposed to balance bike stations.

No / Large-scale behavioral simulations are widely used to study real-world multi-agent systems. Such programs normally run in discrete time-steps or ticks, with simulated space decomposed into domains that are distributed over a set of workers to achieve parallelism. A distinguishing feature of behavioral simulations is their frequent and high-volume group migration, the phenomenon in which simulated objects traverse domains in groups at massive scale in each tick. This results in continual and significant load imbalance among domains. To tackle this problem, traditional load balancing approaches either require excessive load re-profiling and redistribution, which lead to high computation/communication costs, or perform poorly because their statically partitioned data domains cannot reflect load changes brought by group migration. In this paper, we propose an effective and low-cost load balancing scheme, named Inc-part, based on a key observation that an object is unlikely to move a long distance (across many domains) within a single tick. This localized mobility property allows one to efficiently estimate the load of a dynamic domain incrementally, based on merely the load changes occurring in its neighborhood. The domains experiencing significant load changes are then partitioned or merged, and redistributed to redress load imbalance among the workers. Experiments on a 64-node (1,024-core) platform show that Inc-part can attain excellent load balance with dramatically lowered costs compared to state-of-the-art solutions.

Yes / In this study, large-scale fully instrumented laboratory tests were conducted to investigate the behaviour of buried flexible high-density polyethylene (HDPE) pipes, in sand beds with and without voids subjected to incrementally increasing cyclic loading. Voids with a predetermined size were created at one side of the springlines of the pipes, which were buried at variable depths, H, of 1.5, 2.0 and 2.5 times the diameter of the pipe, D. Results showed that increasing the pipe burial depth, H/D, contributed to decreasing the settlement of the footing, deformation of the pipe crown and invert, lateral displacement of the spring-line, and the stress and strain generated along the pipe crown and invert. Void presence led to a significant increase in the footing settlement, which ranged from 3 % up to 18 %, according to H/D. Furthermore, void presence led to a sharp increase in the crown, invert, and spring-line settlements, which ranged from 34 % to 52 %, 10 %–12.5 %, and 13 %–38 %, respectively. Increasing pipe burial depth was found to be highly effective in protecting buried pipes, minimising inevitable consequences of the presence of voids. However, this was combined with an increase in the pressure at the pipe spring-line that led to a positive horizontal support at the pipe’s spring-lines resulting in reducing pipe deformation.

A general framework to design structural systems for a system-reliability goal is proposed. Component-based structural design proceeds on a member to member basis, insuring acceptable failure probabilities for every single structural member without explicitly assessing the overall system safety, whereas structural failure consequences are related to the whole system performance (the cost of a building or a bridge destroyed by an earthquake) rather than a single beam or column failure. Engineering intuition tells us that the system is safer than each individual component due to the likelihood of load redistribution and al- ternate load paths, however such conservatism cannot be guaranteed without an explicit system-level safety check. As a result, component-based structural designs can lead to both over-conservative components and a less-than-anticipated system reliability. System performance depends on component properties as well as the load-sharing network, which can possess a wide range of behaviors varying from a dense redundant system with scope for load redistribution after failure initiates, to a weakest-link type network that fails as soon as the first member exceeds its capacity. The load-sharing network is characterized by its overall system reliability and the system-reliability sensitivity, which quantifies the change in system safety due to component reliability modifications. A general algorithm is proposed to calculate modified component reliabilities using the sensitivity vector for the load-sharing network. The modifications represent an improvement on the structural properties of more critical components (more capacity, better ductility), and provide savings on less important members which do not play a significant role. The general methodology is applied to light steel-framed buildings under seismic loads. The building is modeled with non-linear spring elements representing its subsystems. The stochastic response of this model under seismic ground motions provides load-sharing, system reliability and sensitivity information, which are used to propose target diaphragm and shear wall reliability to meet a building reliability goal. Finally, diaphragm target reliability is used to propose modified component designs using stochastic simulations on geometric and materially non-linear finite-element models including every individual component. This material is based upon work supported by the National Science Foundation under Grant Nos. 1301001 (Virginia Tech), 1301033 (University of Massachusetts, Amherst) and 1300484 (Johns Hopkins University). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily re ect the views of the National Science Foundation. The author is grateful to the industry partner, the American Iron and Steel Institute, for their cooperation. / Ph. D. / This research proposes methods to design engineering networks for acceptable overall safety. Some examples of engineering networks include electrical systems, transportation systems and infrastructural systems. When any such system is designed, the properties of every individual component (size, capacity etc.) are assigned according to cost and safety requirements. However, it is typically very difficult to reliably quantify the overall safety of the entire system, which is technically known as ‘system reliability’. As a result, there are limited options for engineers to adjust the individual component designs within a system to achieve a pre-specified ‘targeted’ system reliability . This dissertation proposes computational and statistical methods to achieve this. The proposed methods are applied to a specific engineering system, namely a two story building subjected to ground shaking resulting from an earthquake. Computer models are developed for different scales of the building, beginning from the full building structure, then its individual floors and walls, and finally the individual components that make up each floor and wall. These models are verified with experimental results spanning all three scales. The verified models are then used to both compute the overall system reliability of the building subjected to earthquake ground shaking, as well as to modify its design component-by-component to achieve a targeted system reliability which is different from the system reliability of the original design. The results indicate that the as-designed reliability of the building system is adequate, but this reliability results from features of the building that are not expected to provide additional safety. The research demonstrates means to obtain this additional safety by redesigning the core functional building components, without relying on the unexpected added safety from ‘non-structural’ components (such as partition walls inside a building). The methods developed herein can be applied to redesign the components of various engineering system networks such that a targeted overall system reliability can be satisfied, resulting in improved performance and life-safety, potentially even at reduced costs.