Saudi Cultural Missions Theses & Dissertations
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Item Embargo Optimized Dynamic Electric Vehicles Charging in Smart Cities(University of Maryland Baltimore County, 2024-11-22) Alaskar, Shorooq Sulaiman; Younis, MohamedRecent technological advances have fueled interest in the development of smart cities where the convenience and health of inhabitants are core objectives. Increased automation and reduced green gas emission are prime means for achieving these objectives. No wonder that there is a major push for the adoption of electrical vehicles. Yet, the deployment of electric vehicles (EVs) is slow-paced. One of the primary obstacles hindering the widespread adoption of EVs is range anxiety, which refers to the fear that an EV will not have sufficient battery charge to reach its destination or a nearby charging station. Additionally, long charging times pose a significant barrier, as recharging an EV battery can take considerably longer compared to refueling a conventional gasoline vehicle, making it less convenient for users. Furthermore, the scarcity of charging infrastructure capable of handling a large number of EVs exacerbates these concerns, deterring potential buyers due to worries about accessibility and convenience. Most of the existing charging techniques require EVs to remain stationary while being charged. Wireless charging has emerged as a viable solution that mitigates such a shortcoming by enabling dynamic EV-to-EV charging. It can also expand the driving range of automobiles if the battery can be continuously charged while the vehicle is in motion, hence extending the traveled distance. EV-to-EV charging provides drivers with greater temporal and spatial flexibility, specifically in densely populated urban regions, while aiding in the reduction of energy consumption. It can also mitigate the burden of the grid during peak loads and optimize power usage during off-peak hours. EV-to-EV charging not only adds convenience to drivers, but it enables a new business model as well, where mobile suppliers may sell their energy to EVs on the road. This dissertation addresses the challenge of the dynamic charging and routing of EVs in smart cities. We first present RIMEC, a Routing for Increased Mobile Energy Charging algorithm that determines an optimized EV travel route for utilizing the Mobile Energy Disseminator (MEDs) in order to maximize the potential energy that can be harvested while minimizing the impact on travel time. Second, we introduce an EV-to-EV charging framework for energy suppliers. The framework opts to maximize the profit, while considering battery degradation and the overhead cost. The optimization is modeled as a time-space network and a dynamic programming-based solution strategy is pursued to optimally pair and route the energy supplier (ES) and requester (ER). Specifically, ES is incentivized to rendezvous ERs at encounter nodes to dispense the requested energy through platooning. The complexity of the problem arises from nonstationary consumers and service supply, which make monitoring and synchronizing the movement of ES and ER spatiotemporally challenging. Third, we tackle the problem of dynamic EV-to-EV charging that aims to maximize the supplier's profitability within a specific timeframe, taking into account overhead costs. Finally, we address the multi-supplier, multi-requester routing problem. We formulate the optimization problem mathematically as a mixed-integer program and develop a local search based-heuristic algorithm. Our objective is to optimize system-level metrics, including profitability and throughput. The simulation results validate the effectiveness of our proposed approach, demonstrating significant improvements in maximizing overall profit while minimizing energy consumption, travel time, and distance for requesters.24 0Item Restricted OPTIMIZATION OF ELECTRIC MOTOR-DRIVETRAIN SYSTEMS FOR HELIOSTATS IN CONCENTRATED SOLAR POWER PLANTS(Georgia Institute of Technology, 2024-12) Qwbaiban, Abdulaziz Mohammed; Habetler, ThomasThe objective of the proposed research is to present a design method to simultaneously optimize the electric motor and drivetrain system for driving heliostats in concentrated solar power (CSP) plants. The ultimate objective of this dissertation is to develop a framework and a prototype for an optimized induction machine explicitly designed for heliostat applications that can be integrated with the speed reducer and the other electric drive components. The integrated system aims to reduce the overall cost of CSP plants to increase its share among renewable energy sources. To start with, the torque capabilities of the system are specified by identifying the moment of inertia and the maximum wind load of a benchmark heliostat, as well as properly defining the extreme motion profile. Secondly, the optimal gear ratio of the drivetrain system can be determined based on inertia matching theory, which can be used to select an off-the-shelf drivetrain with a gear ratio close to the optimal value. Simultaneously, the maximum torque requirement of the induction motor and its axial length can be optimized using Maxwell’s stress tensor theorem. Finally, finite element analysis (FEA) results for the induction motor are used to manufacture a special induction machine dedicated to this application.9 0Item Restricted Optimising Supply Chain Management for International Projects: Insights from Project Managers in Saudi Arabia(Reading University, 2024) Alfawzan, Rakan; Buddhi, PathakGiven the difficulties and mutual reliance on each other, supply chain management is rather important for project management. Supply chain management becomes more difficult and demanding if the project is in a different area that is becoming international. Therefore, this paper addresses the optimisation of supply chain activities in the framework of international projects in Saudi Arabia in account of their emergence under the 2030 Vision. This study employs a mixed-methodology quantitative and qualitative approach using a snowball survey questionnaire with open-ended and close-ended questions to gather the difficulties, strategies, and supply chain resilience assessment. The survey questionnaire obtained responses from 103 participants. The findings revealed noteworthy common difficulties in the Saudi Arabian setting that exceeded other challenges. Furthermore, the findings highlighted typical strategies to get beyond these obstacles. Eventually, the resilience results revealed a flaw in Saudi Arabia's resilience farmwork knowledge. Therefore, the application of these research findings can be applied to the framework of international projects in Saudi Arabia to control supply chain operations effectively. However, The research is limited in the context of Saudi Arabia, which could be useless if applied in another area because of the different surroundings.13 0Item Restricted Sustainable desalination strategies: Techno-economic analysis, life-cycle assessment, and optimization of renewable energy-powered plants in Saudi Arabia(Iowa State University, 2024-06-20) Al Marry, Nassar Hamad; Wright, Mark MbaAddressing global water scarcity amidst escalating energy demands and climate change challenges requires innovative approaches in seawater desalination, particularly in arid regions like Saudi Arabia. This journal article style dissertation comprises a comprehensive exploration of sustainable desalination strategies, focusing on techno-economic analysis, life-cycle assessment, and optimization of renewable energy-powered desalination plants. In this dissertation, Chapter 2 provides a thorough review of recent advancements in desalination technologies, emphasizing the urgent need for sustainable solutions. It highlights the significance of data-driven insights and compares various renewable energy sources for desalination, laying the groundwork for subsequent studies. Chapters 3 and 4 present comparative techno-economic and life-cycle assessments of stand-alone reverse osmosis (RO) desalination plants powered by integrated solar Concentrated Solar Power (CSP) and geothermal energy through Organic Rankine Cycle (ORC). The analyses reveal the superior economic and environmental performance of the integrated CSP-geothermal scenario, demonstrating its potential as a sustainable solution for water scarcity challenges in Saudi Arabia. In Chapter 5, a mathematical optimization approach is employed to minimize the Levelized Cost of Water (LCOW) for the proposed desalination plant. By integrating CSP and geothermal energy, an optimal LCOW value of 0.582 $/m3 is achieved, underscoring the economic and environmental benefits of renewable energy-driven desalination in Saudi Arabia. Collectively, these studies contribute valuable insights into the optimization of renewable energy-powered desalination plants, offering a pathway towards sustainable water management in arid regions.43 0Item Restricted Sustainable and Cost-Effective Work Zone Scheduling on Two-Lane Highways with Managed Lanes(New Jersey Institute of Technology, 2024-04-23) Edrees, Ahmed; Chien, StevenRoadway maintenance projects greatly influence the roadway capacity, resulting in potential traffic disturbances captured by delays. Additionally, costs associated with these projects tend to be exorbitantly extensive. Most agencies and planners try to find a solution that minimizes roadway maintenance costs, traffic delays, crash risks, and environmental impact. Work zones on two-way two-lane roadway typically avoids high-demand periods. Lane-closure scenario is commonly used and converts the open lane into a phantom intersection, alternating two-direction movements on one lane with the help of a flagger or a temporary signal. Alternatively, using shoulders as temporarily managed lanes allows for simultaneous two-way movements with minimal interruptions. This scenario can potentially enhance the efficiency of the work zone by allowing for longer work zone segments and fewer setups, while increasing the project cost due to shoulder preparation cost, which is sensitive to the condition of the existing shoulders and the amount of preparation work needed. This study addresses the feasibility of utilizing managed lanes scenarios for two-way two-lane highways, while previous work focused on assessing and optimizing one-lane scenarios. The objective of this study is to develop a cost optimization algorithm and resilience assessment model for work zone scenarios on two-way two-lane highways. The cost optimization process assesses the trade-offs between agency, user, accident, and emission costs. This study enhances several assumptions and limitations of previously developed models by accounting for hourly demand variations, heavy vehicle presence, and work zone buffer areas. Additionally, this study utilizes the latest models for crash risk predictions as illustrated in the Highway Safety Manual (HSM) and emission rate simulator developed by the Environmental Protection Agency (EPA). The results of the optimization models serve as framework for comparison of potential scheduling schemes by exploring the effects of traffic demand variations, work zone lengths, and project starting times, while taking into consideration scheduling restraints, accident risks, and emission standards.17 0Item Restricted Safety Embedded Optimal Decision Making and Control via Barrier States(Georgia Institute of Technology, 2024-05) Almubarak, Hassan; Theodorou, Evangelos A.; Sadegh, NaderAdvancements in engineering and technologies are confronted with unprecedented challenges to meet often strict safety requirements in its various forms. Barrier methods have been successfully implemented in safety-critical control tasks to enforce safety. Nonetheless, most of the existing work in the literature trades off between performance and safety by relaxing performance objectives or compromising safety or are mostly limited to certain classes of dynamical systems and constraints. The objective of the proposed research is to confront the trade-off between safety restrictions and performance through designing the appropriate mathematical tools used to develop provably safe and robust optimal control and planning for general safety-critical dynamical systems and path constraints. In this thesis, aiming to develop algorithms that efficiently achieve safety and optimality simultaneously, I first build on the foundational work of Control Barrier Functions (CBFs) within an optimal control framework. Realizing the limitations of the current form of CBFs, I then pursue the design of embedded barrier states (BaS) as a means of integrating safety into performance objectives. The proposed technique is subsequently used with various robust control, optimal control and motion planning frameworks where it is shown to be effective, efficient and flexible substantially overcoming the limitations of existing work in the literature. The proposed idea is integrated with various techniques such as the nonlinear quadratic regulators (NLQR), the State-Dependent Riccati Equation (SDRE) to solve the safety-critical infinite horizon optimal control problem, the differential dynamic programming (DDP), model predictive control (MPC) and min-max game theoretic optimal control to develop novel algorithms that produce safety-aware and robust control and decisions. Additionally, the proposed model-based frameworks are extended to the data-drive case in which the dynamics of the control system is learned using Gaussian processes to provide probabilistic safety guarantees. Finally, utilizing recent advances in distributed optimization, the optimal control techniques are applied to solve large multi-agent systems.16 0Item Restricted The Humanitarian Vehicle Routing Problem with Non-Routineness of Trips(Purdue University, 2024-04-22) Alturki, Ibrahim; Lee, SeokcheonThe escalating frequency and impact of natural disasters have necessitated the study of Humanitarian Logistics (HL) optimization to mitigate human and financial losses. This dissertation encompasses three pivotal studies that collectively seek to address some of the numerous gaps identified in the nascent literature of HL optimization, particularly in conflict-ridden and low-security environments. The first study conducts a comprehensive survey on the application of Multi-Criteria Decision Making (MCDM) methods in HL, identifying a significant gap between academic research and practical challenges, and highlighting underexplored areas within multicriteria optimization in HL. The second study introduces innovative deterministic and possibilistic models to improve the safety and security of humanitarian personnel by developing a vehicle routing model that minimizes the predictability of trips, a novel aspect in HL research. This includes the introduction of the Humanitarian Vehicle Routing Problem with Non-Routineness of Trips (HVRPNRT), creation of a unique index to measure trip routineness and the provision of an approximate closed-form solution for the aid allocation subproblem, and introduces a novel case study from the ongoing civil unrest in South Sudan. The third study presents a novel heuristic solution algorithm for the HVRPNRT, which is the first of its kind, and outperforms the commercial solver CPLEX on some instances. This algorithm offers near-optimal solutions with reduced computational times and maintains feasibility under stringent security conditions, thereby advancing the field of security-aware HL optimization. Collectively, these studies offer significant contributions to the field of HL optimization, providing a recent through survey of the field, novel practical models, methodologies, and an algorithm that address both operational efficiency and security challenges, in an effort to bridge the gap between theoretical research and real-world humanitarian needs.35 0Item Restricted OPTIMISING RESOURCE ALLOCATION AND OFFLOADING FOR LONG-TERM LOAD BALANCING SOLUTIONS IN FOG COMPUTING NETWORKS(University of Technology Sydney, 2024-02-02) Sulimani, Hamza; Prasad, MukeshNowadays, most emerging critical IoT applications have unique requirements and restrictions to operate efficiently; otherwise, they could be useless. Latency is one of these requirements. Fog computing (FC) is the complement system for cloud computing, proving it is the ideal computing environment for critical IoT applications. Distributed computing systems, such as FC, have an inherent problem when the computing units have different computing loads, called load difference problems. Offloading and service placement are some techniques used to fix these problems. Although prevalent offloading is the appropriate technique for this research, its procedures generate hidden costs in a system, such as decision time, distant offloading, and network congestion. Many researchers attempt to reduce these costs to get the results of static offloading (in stable environments). However, this research seeks to overcome the hidden costs in the prevalent offloading techniques to balance the load in a fog environment by utilising the sustainability concept. This research believes that increasing physical resources is the only way to improve efficiency as a long-term solution. The study consists of two consecutive phases. The first phase attempts to find the optimum solution between task offloading and service placement. The solution must revive the low-cost offloading solution. A sustainable load-balancing monitoring system (SlbmS) represents the second phase of this research. It is the comprehensive solution for the optimum solution to release its limitations. SlbmS uses the sustainability concept to solve the problem of the limitation of resources in edge computing using reinforcement learning. The experiment results of the two phases show that hybrid offloading outperforms the service placement policy in the first stage and prevalent offloading in the second stage when utilising the behaviour of static offloading to reduce the offloading costs in unpredictable environments. The study aims to explore a new area of research that attempts to amend the network topology to improve resource provisioning to provide a free resource at the network's edge. This research paved the way for a new dimension of analysis. It is the first research to recommend the physical expansion in the fog layer using the sustainability concept.18 0Item Restricted Optimal Novel Taxiing Navigation of a BOEING-747 Aircraft Using Artificial Intelligence(Saudi Digital Library, 2024-01-16) Alhindi, Abrar; Mahfouf, MahdiAircraft ground movement coordination plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the centre of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This thesis proposes new approaches using Artificial Intelligence (AI) for optimal taxiing navigation of a high-fidelity aircraft model, working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines. The proposed approaches used in this thesis are: PID controller, artificial neural networks controller, Fuzzy Inference System (FIS) model and an online controller using reinforcement learning. The proposed approaches integrate a MATLAB-Simulink model of the BOEING-747 aircraft with artificial intelligence based control that successfully generate fuel-efficient four- dimensional trajectories 4DTs in real time, while taking constraints on operations into account. The proposed methodologies are realistic and simple to implement. Moreover, simulation studies show that the proposed approaches are capable of providing a reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.34 0Item Restricted Optimizing the Selection of COVID-19 Vaccine Distribution Centers and Allocation Quantities: A Case Study for the County of Los Angeles(2023-07) Aljohani, Basim; Hall, RandolphThis thesis aims to provide a tool to help policy and decision-makers establish well-informed plans about the selection and distribution of locations and quantities of vaccines. Optimization of vaccine distribution centers' locations plays a crucial role in providing communities with easy access to vaccines, which will help in controlling global pandemics, such as the recent COVID-19, and mitigate the risks of losing lives and economic losses. As studies have proven, strategic planning of the vaccine site locations and the allocated quantities of the vaccines could help boost the amount of vaccine uptake. This thesis utilizes mathematical modeling techniques to develop a mixed integer program that aims to minimize travel time, distance, and associated costs in one of the largest counties in the United States, Los Angeles County. The developed model takes into account the diverse demographics and socioeconomic factors of the County and plans for the selection and allocations accordingly. The model explores 277 zip codes within Los Angeles and analyzes them as potential vaccine distribution centers. It also incorporates the two different and most common means of transportation, cars, and public transit, to account for all users. Three scenarios are explored where each zip code of the 277 is assigned priority based on the following factors: population, Healthy Places Index, and a Vulnerability to COVID-19 index. The output showed significant improvements in reducing average travel times and distances as well as savings in costs when compared to the actual selected sites within the County.15 0