Background The Sustainable Development Goals provides targets that foster greater mobilization of global resources and efforts. SDG Goal 3 Ensure healthy lives and promote well-being for all at all ages, sets targets for the reduction of maternal mortality rates and mortality rates for children under-five. Health insurance coverage is thought to provide access to needed primary services to accomplish these goals. West Africa is the region of the world with the highest burden of disease and it is unclear if insurance coverage does provide needed access to services. The articles within this thesis examine whether or not health insurance provides greater access to primary services for mothers and children, while determining other factors to be considered. Method For the systematic review, we carried out a search on four databases. Eligible studies included mother’s under-five and children in West Africa. The primary outcome was insurance impacting the rate of utilization of services. Data was extracted using standardized form, and methodology was assessed using the Joanna Briggs Institute forms. Our cross-sectional study used DHS data from 10 West African countries. Data was cleaned, weighed and analyzed using Stata. The independent variable was health insurance, and the variable of outcome was making a minimum of four antenatal care visits. Data was analyzed using binary logistic regression and we presented results using crude and adjusted odds ratio at 95% confidence interval. Results The narrative synthesis was chosen for the review. We found that in most study settings, insurance increased access to services. The cross-sectional study found that women with insurance were more likely to make the recommended number of ANC visits than their uninsured counterparts (aOR [95% CI] =1.55 [1.37-1.73]). Socio-economic status also impact access to services. Conclusion Health insurance does increase access to services and should be pursued as a viable long-term policy, but access is still dependent on socio-economic status. Due to the COVID-19 pandemic, burden of disease of the region and systems challenges, other solutions should be pursued in the near-term. Future investigation should consider the role of equity as a guiding principle.

Kaffe Bueno, ett bioteknologiskt start-up-företag dedikerat till framsteg inom naturliga och gröna produkter, fokuserar på att utnyttja den outnyttjade potentialen hos kaffebiprodukter genom grön kemi och bioteknik. Då endast 1% av kaffet används för bryggning, extraherar företaget lösliga och olösliga produkter genom en uppdelning mellan ett oljeutvinning och ett efterföljande vattenuttag, vilket bidrar till att sänka CO2-utsläppen per värde och en cirkulär ekonomi. Inom hudvård har Kaffe Bueno tidigare utvecklat en BB-kräm med återvunnen kaffesump för pigmentering och hudvårdsfördelar, men stötte på utmaningar när det gäller täckning jämfört med syntetiska BB-krämer, vilket ledde till målet med detta examensarbete. Målet med denna studie är att formulera en helt växtbaserad BB-kräm med optimal täckning, överträffa standarden satt av syntetiska kemiska BB-krämer. Den centrala fråga som styr forskningen är: "Vilka är de optimala formuleringsrstrategierna och växtbaserade ingredienserna för att uppnå högsta täckning i en helt växtbaserad BB-kräm samtidigt som stabilitet säkerställs?". Denna forskning stämmer överens med den globala kosmetikindustrins växande efterfråga av hållbara, växtbaserade formuleringar, som svar på konsumenternas preferenser för miljövänliga produkter. Forskningen inkluderar utveckling av en växtbaserad vit baskräm utan titanoxid eller zinkoxid, och sedan användning av naturliga pigment som KAFFAGE® för att justera färgen, säkerställa hållbarhet, täckning och stabilitet över tid, vid ljusexponering och användning. Sammanfattningsvis formulerades under denna studie framgångsrikt en växtbaserad BB-kräm med täckning jämförbar med marknadsstandarder, undvikande av traditionella järnoxider och med användning av hållbara ingredienser som KAFFAGE-BD®-pigment, även om användning av titandioxid (TiO2) fortfarande krävs. Forskningen betonade vikten av texturoptimering, färgmatchning och sensorisk perception, vilket positionerar BB-krämen som en pålitlig och mångsidig kosmetisk lösning. Framtida forskning bör prioritera undersökning av alternativ till TiO2, såsom mikrokristallin cellulosa, för att förbättra hållbarheten i BB-krämens formulering. / Kaffe Bueno, a bioscience startup company dedicated to advancing natural and green products, focuses on harnessing the untapped potential of coffee by-products through green chemistry and biotechnology. With only 1% of coffee utilized for brewing, the company extracts soluble and insoluble products through a partion between an oil extraction and a subsequent water extraction, contributing to lowering the CO2 emissions per value from coffee consumption and a circular economy. In skincare, Kaffe Bueno previously developed a Beauty Balm cream using ingredients from upcycled spent coffee grounds for pigmentation and skincare benefits, but faced challenges in achieving coverage comparable to synthetic BB creams, prompting the goal of this master thesis. The objective of this study is to formulate an all plant-based BB cream with optimal coverage, surpassing the standard set by synthetic BB creams. The central question guiding the research is: "What are the optimal formulation strategies and plant-based ingredients for achieving the highest coverage in an all plant-based BB cream while ensuring stability?". This research aligns with the global cosmetic industry's growing demand for sustainable, plant-based formulations, responding to consumer preferences for eco-friendly products. The research includes developing a white plant-based base cream without titanium dioxide or zinc oxide, and then utilizing natural pigments like KAFFAGE® to adjust the tint, ensuring sustainability, up-to-standard coverage, stability over time, light exposition and use. Tests conducted to validate the product’s performance and quality include a stability test, a sensory panel evaluation, a sweat test, and other relevant assessments to ensure the cream's effectiveness, user satisfaction, and compliance with industry standards. In conclusion, this study successfully formulated a plant-based BB cream with coverage comparable to market benchmarks, circumventing traditional iron oxides and relying on sustainable ingredients like KAFFAGE-BD®, even though the use of titanium dioxide (TiO2) is still needed. The research emphasized the importance of texture optimization, color matching, and sensory perception, highlighting the essential attributes for BB creams in general and positioning this particular BB cream as a reliable and versatile cosmetic solution. Future research should prioritize exploring alternatives to TiO2, such as microcrystalline cellulose, to enhance sustainability in BB cream formulations.

Multiple-input/multiple-output (MIMO) systems with the adaptive array processing technique, also referred to as smart antennas, have received extensive attention in wireless communications due to their ability to combat multipath fading and co-channel interference, two major channel impairments that degrade system performance. However, when smart antennas are deployed in wireless networks, careful attention is required since any defective or imperfect operation of smart antennas can severely degrade the performance of the entire network. Therefore, the evaluation of network performance under ideal and imperfect conditions is critical in the process of system design and should precede deploying smart antennas on the wireless network. This work focuses on the development of an analytical framework to evaluate the performance of wireless networks based on popular DS/CDMA cellular systems equipped with antenna arrays. Spatial diversity at both the base station (BS) and the mobile station (MS) is investigated through both analytical analysis and simulation. The main contribution of this research is to provide a comprehensive analytical framework for examining the system level performance with multiple antennas at both the BS and the MS. Using the framework developed in this research, system capacity and coverage of the uplink (or reverse link) are investigated when antenna arrays are implemented at both the BS and the MS. In addition, the system capacity and soft handoff capability of the downlink (or forward link) are examined taking into account MIMO. Furthermore, various physical and upper layer parameters that can affect the system level performance are taken into account in the analytical framework and their combined impact is evaluated. Finally, to validate the analytical analysis results, a system level simulator is developed and selective results are provided. / Ph. D.

In simple linear regression, it is assumed that two variables are linearly related, with unknown intercept and slope parameters. In particular, a regressor variable is assumed to be precisely measurable, and a response is assumed to be a random variable whose mean depends on the regressor via a linear function. For the simple linear regression problem, interest typically centers on estimation of the unknown model parameters, and perhaps application of the resulting estimated linear relationship to make predictions about future response values corresponding to given regressor values. The linear statistical calibration problem (or, more precisely, the absolute linear calibration problem), bears a resemblance to simple linear regression. It is still assumed that the two variables are linearly related, with unknown intercept and slope parameters. However, in calibration, interest centers on estimating an unknown value of the regressor, corresponding to an observed value of the response variable. We consider Bayesian methods of analysis for the linear statistical calibration problem, based on noninformative priors. Posterior analyses are assessed and compared with classical inference procedures. It is shown that noninformative prior Bayesian analysis is a strong competitor, yielding posterior inferences that can, in many cases, be correctly interpreted in a frequentist context. We also consider extensions of the linear statistical calibration problem to polynomial models and multivariate regression models. For these models, noninformative priors are developed, and posterior inferences are derived. The results are illustrated with analyses of published data sets. In addition, a certain type of heteroscedasticity is considered, which relaxes the traditional assumptions made in the analysis of a statistical calibration problem. It is shown that the resulting analysis can yield more reliable results than an analysis of the homoscedastic model. / Ph. D.

This dissertation addresses two important problems in reusing intellectual properties (IPs) in the form of reusable design or verification components. The first problem is associated with fast and effective integration of reusable design components into a System-on-chip (SoC), so faster design turn-around time can be achieved, leading to faster time-to-market. The second problem has the same goals of faster product design cycle, but emphasizes on verification model reuse, rather than design component reuse. It specifically addresses reuse of reusable verification IPs to enable a "write once, use many times" verification strategy. This dissertation is accordingly divided into part I and part II which are related but describe the two problems and our solutions to them. These two related but distinctive problems faced by system design companies have been tackled through a unique approach which hither-to-fore only have been used in the software engineering domain. This approach is called metamodeling, which allows creating customized meta-language to describe the syntax and semantics for a modeling domain. It provides a way to create, transform and analyze domain specific languages, which are themselves described by metamodels, and the transformation and processing of models in such languages are also described by metamodels. This makes machine based interpretation and translation from these models an easier and formal task. In part I, we consider the problem of rapid system-level model integration of existing reusable components such that (i) the required architecture of the SoC can be expressed formally, (ii) automatic selection of components from an IP library to match the need of the system being integrated can be done, (iii) integrability of the components is provable, or checkable automatically, and (iv) structural and behavioral type systems for each component can be utilized through inferencing and matching techniques to ensure their compatibility. Our solutions include a component composition language, algorithms for component selection, type matching and inferencing algorithms, temporal property based behavioral typing, and finally a software system on top of an existing metamodeling environment. In part II, we use the same metamodeling environment to create a framework for modeling generative verification IPs. Our main contributions relate to INTEL's microprocessor verification environment, and our solution spans various abstraction levels (System, architectural, and microarchitecture) to perform verification. We provide a unified language that can be used to model verification IPs at all abstraction levels, and verification collaterals such as testbenches, simulators, and coverage monitors can be generated from these models, thereby enhancing reuse in verification. / Ph. D.

In this dissertation, guidance and control of autonomous unmanned aerial systems (UAS) are explored. Specifically, we investigate model reference adaptive control (MRAC) based systems for tailsitter UAS, and guidance and control of multi-rotor UAS for tactical maneuvering and coverage. Applications, both current and potential, are investigated and gaps in existing technologies are identified. To address the controls problem of a particular class of tailsitter UAS, that is, quadrotor-biplanes, subject to modeling uncertainties, unmodeled payloads, wind gusts, and actuator faults and failures, two approaches are developed. In the first approach, the longitudinal dynamics of a tailsitter UAS are regulated using an MRAC law for prescribed performance and output tracking in a novel control architecture. The MRAC law for prescribed performance and output tracking incorporates a Linear Quadratic Regulator (LQR) baseline controller using integral-feedback interconnections. Constraints on the trajectory tracking error are enforced using barrier Lyapunov functions, and a user-defined rate of convergence of the trajectory tracking error is guaranteed by employing a reference model for the trajectory tracking error's transient dynamics. In this control system, the translational and rotational dynamics are split into an outer loop and an inner loop, respectively, to account for the underactuation of the quadrotor-biplane. In the outer loop, estimates of the aerodynamic forces and MRAC laws are used to stabilize the translational dynamics. Furthermore, the reference pitch angle is deduced such that the vehicle's total thrust never points towards the Earth for safety, and discontinuities inherent to the signed arctangent function commonly used for determining orientations are avoided. In the inner loop, estimates of the aerodynamic moment and an MRAC law are used to stabilize the rotational dynamics. A law for determining the desired total thrust is proposed, which ensures that if the vehicle's orientation is close enough to the desired orientation, then the proper thrust force is applied. A control allocation scheme is presented to ensure that the desired moment of the thrust force is always realized, and constraints on the non-negativity of the thrust force produced by the actuators are satisfied. The proposed control architecture employing MRAC for prescribed performance and output signal tracking is validated in simulation, and the MRAC law for prescribed performance is compared to the classical MRAC law. In the second approach, a unified control architecture based on MRAC is presented which does not separate the longitudinal and lateral-directional dynamics. The translational and rotational dynamics are separated into outer and inner loops, respectively, to address the underactuation of the tailsitter UAS. Since it is expected that the vehicle will undergo large rotations, the tailsitter's orientation is captured using quaternions, which are singularity-free. Furthermore, the windup phenomenon is addressed by employing barrier Lyapunov functions to ensure that the first component of the tracking error quaternion is positive, and thus, the shortest rotation is followed to drive the vehicle's current orientation to the reference orientation. In the outer loop, the desired thrust force is determined using estimates of the aerodynamic forces and an MRAC law. The reference orientation is determined as a solution of the orthogonal Procrustes' problem, which finds the smallest rotation from the current orientation of the thrust force, to the orientation of th desired thrust force. The angular velocity and acceleration cannot be deduced by taking the time derivative of the solution of the orthogonal Procrustes' problem due to the discontinuous nature of the singular value decomposition. Therefore, the twice continuously differentiable function, spherical linear interpolation, is used to find a geodesic joining the unit quaternion capturing the vehicle's current orientation, and the unit quaternion capturing the reference orientation. An interesting result is that the angular velocity and acceleration depend only on the first and second derivatives of the scalar-valued function which parameterizes the spherical linear interpolation function; the actual function is immaterial. However, determining the shape of this function is nontrivial, and hence, an approach inspired by model predictive control is used. In the inner loop, estimates of the aerodynamic moment and an MRAC law are used to stabilize the rotational dynamics, and the thrust force is allocated to the individual propellers. The validity of the proposed control scheme is presented in simulation. An integrated guidance and control system for autonomous UAS is proposed to maneuver in an unknown, dynamic, and potentially hostile environment in a reckless or tactical manner as prescribed by the user. Tactical maneuvering in this guidance and control system is enabled through exploitation of obstacles in the environment for shelter as the vehicle approaches its goal. Reckless maneuvering is enabled by ignoring the presence of nearby obstacles while proceeding towards the goal, while remaining collision-free. The demarcation of reckless and tactical behaviors are bio-inspired, since these tactics are used by animals or ground-based troops. The guidance system fuses a path planner, collision-avoidance algorithm, vision-based navigation system, and a trajectory planner. The path planner is based on the A* search algorithm, and a custom tunable cost-to-come and heuristic function are proposed to enable the exploitation of the obstacles' set for shelter by decreasing the weight of edges in the underlying graph that capture nodes close to the obstacles' set. The consistency of the heuristic is established, and hence, the search algorithm will return an optimal solution, and not expand nodes multiple times. In realistic scenarios, fast replanning is necessary to ensure that the system realizes the desired behavior, and does not collide with obstacles. The trajectory planner is based on fast model predictive control (fMPC), and thus, can be executed in real time. A custom tunable cost function, which weighs the importance of proximity to the obstacles' set and proximity to the goal, is employed to provide another mechanism for enabling tactical behaviors. The novel collision avoidance algorithm is based on the solution of a particular class of semidefinite programming problems, that is, quadratic discrimination. The collision avoidance algorithm produces convex sets of free space near the UAS by finding ellipsoids that separate the UAS from the obstacles' set. The convex sets are used in the fMPC framework as inequality constraints. The collision avoidance algorithm's computational burden is determined empirically, and is shown to be faster than two similar algorithms in the literature. The modules above are integrated into a single guidance system, which supplies reference trajectories to an arbitrary control system, and the validity of the proposed approach is exhibited in several simulations and flight tests. Furthermore, a taxonomy of flight behaviors is presented to understand how the tunable parameters affect the recklessness or stealthiness of the resulting trajectory. Lastly, an integrated guidance and control system for autonomous UAS performing tactical coverage in an unknown, dynamic, and potentially hostile environment in a reckless or tactical manner as prescribed by the user is presented. The guidance problem for coverage concerns strategies and route planning for gathering information about an environment. The aim of gathering information about an unknown environment is to aid in situational awareness and planning for service organizations and first-responders. To address this problem, goal selection, path planning, collision avoidance, and trajectory planning are integrated. A novel goal selection algorithm based on the Octree data structure is proposed to autonomously determine goal points for the path planner. In this algorithm, voxel maps deduced by a navigation system, which capture the occupancy and exploration status of areas of the environment, are segmented into partitions that capture large unexplored areas, and large explored areas. Large unexplored areas are used as candidates for goal points. The feasibility of goal points is determined by employing a greedy $A^*$ technique. The algorithm boasts tunable parameters that allow the user to specify a greedy or systematic behavior when determining a sequence of goal points. The computational burden of this technique is determined empirically, and is shown to be useful for real-time use in realistic scenarios. The path planner is based on the Lifelong Planning $A^*$ ($LPA^*$) search algorithm which is shown to have advantages over the $A^*$ technique. A custom tunable cost-to-come and heuristic function are proposed to enable tactical or reckless path planning. A novel collision avoidance algorithm is proposed as an improved version of the aforementioned collision avoidance algorithm, where the volume of the resulting constraint sets are improved, and thus, more of the free space is captured by the convex set, and hence, the trajectory planner can exploit more of the environment for tactical maneuvering. This algorithm is based on semidefinite programming and a fast approximate convex hull algorithm. The trajectory planner is based on fMPC, employs a custom cost function to enable tactical maneuvering by coasting the surface of obstacles and regulation of the desired acceleration as a function of proximity to shelter, employs barrier functions to constrain the attitude of the vehicle and ensure thrust positivity, and employs a quadrotor UAS' output feedback linearized equations of motion as differential constraints to enable aggressive maneuvering. The efficacy of the proposed system is validated using a custom-made C++ simulator. / Doctor of Philosophy / In recent years, unmanned aerial systems (UAS) such as quadcopters, hexacopters, and octocopters, have seen increased popularity for a myriad of applications including crop monitoring, photography, surveying, surveillance, wireless network extension, search and rescue, firefighter support, and military operations, to name a few. This list of applications stems from UAS' maneuverability, adaptability, accessibility, and their absence of an onboard pilot. While some of these applications can be executed with current capabilities, the performance of these systems could be improved, and there are many applications where UAS could be used to fulfil substantial roles in areas such as logistics, tactical surveillance, and direct human-interaction. However, these applications require a considerable improvement in autopilot design for UAS; shortcomings of current capabilities are identified in this thesis. Indeed, one of the most important improvements to be made is enabling fully autonomous operations where limited human intervention and oversight is necessary for mission success. In this thesis, we present two adaptive control systems for tailsitter UAS to enable accurate trajectory tracking in realistic scenarios with degraded conditions, such as inclement weather with unsteady winds, poorly-modeled dynamics as a result of negligence or a cost-benefit analysis, failing actuators due to overuse or damage from collisions. In the first adaptive control system, we focus on the tailsitter UAS' longitudinal dynamics, and employ a novel adaptive control technique to stabilize the system. In the second adaptive control system, we do not separate the longitudinal and lateral-directional dynamics, and split the tailsitter UAS' translational and rotational dynamics into outer and inner loops, respectively. In this control system, the windup problem is addressed using barrier functions, the reference orientation is determined as a solution to the orthogonal Procrustes' problem, and the system's dynamics are stabilized using model reference adaptive control. Furthermore, in this dissertation, we develop and present a guidance and control system which can be used to enable autonomous intelligence, surveillance, reconnaissance, and logistics (ISRL) operations in unknown, dynamic, and potentially hostile environments. The guidance system enables the UAS to achieve a user-defined behavior which ranges from tactical to reckless. The tactical or reckless behaviors are enabled through the guidance system's path planner, which is based on the A* search algorithm employing custom cost and heuristic function. Similarly, the guidance system's trajectory planner, which is based on fast model predictive control (fMPC), enables tactical or reckless behaviors through a custom cost function. The problem of collision-avoidance is handled through the path planner, which returns collision-free paths, and a novel constraint set generation algorithm which deduces regions of free space near the UAS; these regions are used as constraint sets for the trajectory planner. We validate the proposed approach in simulation and flight tests, and present a taxonomy of flight behaviors.

The DIRECT (DIviding RECTangles) algorithm JONESJOTi, a variant of Lipschitzian methods for bound constrained global optimization, has been applied to the optimal transmitter placement for indoor wireless systems. Power coverage and BER (bit error rate) are considered as two criteria for optimizing locations of a specified number of transmitters across the feasible region of the design space. The performance of a DIRECT implementation in such applications depends on the characteristics of the objective function, the problem dimension, and the desired solution accuracy. Implementations with static data structures often fail in practice because of unpredictable memory requirements. This is especially critical in S⁴W (Site-Specific System Simulator for Wireless communication systems), where the DIRECT optimization is just one small component connected to a parallel 3D propagation ray tracing modeler running on a 200-node Beowulf cluster of Linux workstations, and surrogate functions for a WCDMA (wideband code division multiple access) simulator are also used to estimate the channel performance. Any component failure of this large computation would abort the entire design process. To make the DIRECT global optimization algorithm efficient and robust, a set of dynamic data structures is proposed here to balance the memory requirements with execution time, while simultaneously adapting to arbitrary problem size. The focus is on design issues of the dynamic data structures, related memory management strategies, and application issues of the DIRECT algorithm to the transmitter placement optimization for wireless communication systems. Results for two indoor systems are presented to demonstrate the effectiveness of the present work. / Master of Science

Software testing is important, but judging whether a set of software tests is effective is difficult. This problem also appears in the classroom as educators more frequently include software testing activities in programming assignments. The most common measures used to assess student-written software tests are coverage criteria—tracking how much of the student’s code (in terms of statements, or branches) is exercised by the corresponding tests. However, coverage criteria have limitations and sometimes overestimate the true quality of the tests. This dissertation investigates alternative measures of test quality based on how many defects the tests can detect either from code written by other students—all-pairs execution—or from artificially injected changes—mutation analysis. We also investigate a new potential measure called checked code coverage that calculates coverage from the dynamic backward slices of test oracles, i.e. all statements that contribute to the checked result of any test. Adoption of these alternative approaches in automated classroom grading systems require overcoming a number of technical challenges. This research addresses these challenges and experimentally compares different methods in terms of how well they predict defect-detection capabilities of student-written tests when run against over 36,500 known, authentic, human-written errors. For data collection, we use CS2 assignments and evaluate students’ tests with 10 different measures—all-pairs execution, mutation testing with four different sets of mutation operators, checked code coverage, and four coverage criteria. Experimental results encompassing 1,971,073 test runs show that all-pairs execution is the most accurate predictor of the underlying defect-detection capability of a test suite. The second best predictor is mutation analysis with the statement deletion operator. Further, no strong correlation was found between defect-detection capability and coverage measures. / Ph. D.

Considerable research has been directed towards efficient test stimuli generation for Register Transfer Level (RTL) circuits. However, stimuli generation frameworks are still not capable of generating effective stimuli for all circuits. Some of the limiting factors are 1) It is hard to ascertain if a branch in the RTL code is reachable, and 2) Some hard-to-reach branches require intelligent algorithms to reach them. Since unreachable branches cannot be reached by any test sequence, we propose a method to deduce unreachability of a branch by looking for the possible values which a signal can take in an RTL code without explicit unrolling of the design. To the best of our knowledge, this method has been able to identify more unreachable branches than any method published in this domain, while being computationally less expensive. Moreover, some branches require very specific values on input signals in specific cycles to reach them. Conventional symbolic execution can generate those values but is computationally expensive. We propose a cycle-by-cycle restrictive symbolic execution that analyzes only a selected subset of program statements to reduce the computational cost. Our proposed method gathers information from an initial execution trace generated by any technique, to intelligently decide specific cycles where the application of this method will be helpful. This method can hybrid with simulation-based test stimuli generation methods to reduce the cost of formal verification. With this method, we were able to reach some previously unreached branches in ITC99 benchmark circuits. / Master of Science

A dual satellite system in a Low Earth Orbit, LEO, would be beneficial to study the electromagnetic occurrences in the magnetosphere and their contributions to the development of the aurora events in the Earth's lower atmosphere. An orbit configuration is sought that would increase the total time that both satellites are inside the auroral oval. Some additional objectives include minimizing the total fuel cost and the average angle between the satellites' radius vectors. This orbit configuration is developed using a series of instantaneous burns applied at each satellite's perigee. An analysis of the optimal solutions generated by a Particle Swarm Optimization method is completed using a cost function with different weights for the time, fuel, and angle terms. Three different scenarios are presented: a single burn case, a double burn case, and a four burn case. The results are calculated using two different orbital mechanics models: an unperturbed two-body simulation and a two-body simulation with added Earth's equatorial bulge effects. It is shown that the added perturbation reduces the total event time in the optimal solutions generated. Specific weights for the cost function are recommended for further studies. / Master of Science