A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Equilibrium Programming for Improved Management of Water-Resource Systems(2024) Boyd, Nathan Tyler; Gabriel, Steven A; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Effective water-resources management requires the joint consideration of multiple decision-makers as well as the physical flow of water in both built and natural environments. Traditionally, game-theory models were developed to explain the interactions of water decision-makers such as states, cities, industries, and regulators. These models account for socio-economic factors such as water supply and demand. However, they often lack insight into how water or pollution should be physically managed with respect to overland flow, streams, reservoirs, and infrastructure. Conversely, optimization-based models have accounted for these physical features but usually assume a single decision-maker who acts as a central planner. Equilibrium programming, which was developed in the field of operations research, provides a solution to this modeling dilemma. First, it can incorporate the optimization problems of multiple decision-makers into a single model. Second, the socio-economic interactions of these decision-makers can be modeled as well such as a market for balancing water supply and demand. Equilibrium programming has been widely applied to energy problems, but a few recent works have begun to explore applications in water-resource systems. These works model water-allocation markets subject to the flow of water supply from upstream to downstream as well as the nexus of water-quality management with energy markets. This dissertation applies equilibrium programming to a broader set of physical characteristics and socio-economic interactions than these recent works. Chapter 2 also focuses on the flow of water from upstream to downstream but incorporates markets for water recycling and reuse. Chapter 3 also focuses on water-quality management but uses a credit market to implement water-pollution regulations in a globally optimal manner. Chapter 4 explores alternative conceptions for socio-economic interactions beyond market-based approaches. Specifically, social learning is modeled as a means to lower the cost of water-treatment technologies. This dissertation's research contributions are significant to both the operations research community and the water-resources community. For the operations research community, this dissertation could serve as model archetypes for future research into equilibrium programming and water-resource systems. For instance, Chapter 1 organizes the research in this dissertation in terms of three themes: stream, land, and sea. For the water-resources community, this dissertation could make equilibrium programming more relevant in practice. Chapter 2 applies equilibrium programming to the Duck River Watershed (Tennessee, USA), and Chapter 3 applies it to the Anacostia River Watershed (Washington DC and Maryland, USA). The results also reinforce the importance of the relationships between socio-economic interactions and physical features in water resource systems. However, the risk aversion of the players acts as an important mediating role in the significance of these relationships. Future research could investigate mechanisms for the emergence of altruistic decision-making to improve equity among the players in water-resource systems.Item REAL-TIME DISPATCHING AND REDEPLOYMENT OF HETEROGENEOUS EMERGENCY VEHICLES FLEET WITH A BALANCED WORKLOAD(2023) Fang, Chenyang; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The emergency management service (EMS) system is a complicated system that tries to coordinate each system component to provide a quick response to emergencies. Different types of vehicles cooperate to finish the tasks under unified command. The EMS system tries to respond quickly to emergency calls and communicate with each department to balance the resources and provide maximal coverage for the whole system. This work aims to develop a highly efficient model for the EMS system to assist the coordinator in making the dispatching and relocation decisions simultaneously. Meanwhile, the model will make a route decision to provide the vehicle drivers with route guidance. In the model, heterogenous emergency vehicle fleets consisting of police vehicles, Basic Life Support (BLS) vehicles, Advanced Life Support (ALS) vehicles, Fire Engines, Fire Trucks, and Fire Quants are considered. Moreover, a coverage strategy is proposed, and different coverage types are considered according to the division of vehicle function. The model tries to provide maximal coverage by advanced vehicles under the premise of ensuring full coverage by basic vehicles. The workload balance of the vehicle crews is considered in the model to ensure fairness. A mathematical model is proposed, then a numerical study is conducted to test the model's performance. The results show that the proposed model can perform well in large-scale problems with significant demands. A comprehensive analysis is conducted on the real-case historical medical data. Then a discrete event simulation system is built. The framework of a discrete event simulation model can mimic the evolution of the entire operation of an emergency response system over time. Finally, the proposed model and discrete event simulation system are applied to the real-case historical medical data. Three different categories of performance measurements are collected, analyzed, and compared with the real-case data. A comprehensive sensitivity analysis is conducted to test the ability of the model to handle different situations. The final results illustrate that the proposed model can improve overall performance in various evaluation metrics.Item THREE ESSAYS ON OPTIMIZATION, MACHINE LEARNING, AND GAME THEORY IN ENERGY(2023) Chanpiwat, Pattanun; Gabriel, Steven A.; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation comprises three main essays that share a common theme: developing methods to promote sustainable and renewable energy from both the supply and demand sides, from an application perspective. The first essay (Chapter 2) addresses demand response (DR) scheduling using dynamic programming (DP) and customer classification. The goal is to analyze and cluster residential households into homogeneous groups based on their electricity load. This allows retail electric providers (REPs) to reduce energy use and financial risks during peak demand periods. Compared to a business-as-usual heuristic, the proposed approach has an average 2.3% improvement in profitability and runs approximately 70 times faster by avoiding the need to run the DR dynamic programming separately for each household. The second essay in Chapter 3 analyzes the integration of renewable energy sources and battery storage in energy systems. It develops a stochastic mixed complementarity problem (MCP) for analyzing oligopolistic generation with battery storage, taking into account both conventional and variable renewable energy supplies. This contribution is novel because it considers multi-stage stochastic MCPs with recourse decisions. The sensitivity analysis shows that increasing battery capacity can reduce price volatility and variance of power generation. However, it has a small impact on carbon emissions reduction. Using a stochastic MCP approach can increase power producers' profits by almost 20 percent, as proposed by the value of stochastic equilibrium solutions. Higher battery storage capacity reduces the uncertainty of the system in all cases related to average delivered prices. Nevertheless, investing in enlarging battery storage has diminishing returns to producers' profits at a certain point restricted by market limitations such as demand and supply or pricing structure. The third essay (Chapter 4) proposes a new practical application of the stochastic dual dynamic programming (SDDP) algorithm that considers uncertainties in the electricity market, such as electricity prices, residential photovoltaic (PV) generation, and loads. The SDDP model optimizes the scheduling of battery storage usage for sequential decision-making over a planning horizon by considering predicted uncertainty scenarios and their associated probabilities. After examining the benefits of shared battery storage in housing companies, the results show that the SDDP model improves the average objective function values (i.e., costs) by approximately 32% compared to a model without it. The results also indicate that the mean objective function values at the end of the first stage of the proposed SDDP model with battery storage and the deterministic LP model equivalent (with perfect foresight) with battery storage differ by less than 30%. The models and insights developed in this dissertation are valuable for facilitating energy policy-making in a rapidly evolving industry. Furthermore, these contributions can advance computational techniques, encourage the use and development of renewable energy sources, and increase public education on energy efficiency and environmental awareness.Item A NOVEL APPLICATION OF SELECT AGILE CONCEPTS AND STOCHASTIC ANALYSIS FOR THE OPTIMIZATION OF TRAINING PROGRAMS WITHIN HIGH RELIABILITY ORGANIZATIONS IN HIGH TURN-OVER ENVIRONMENTS AT EDUCATIONAL INSTITUTES AND IN INDUSTRY(2023) Blanton, Richard L; Cui, Qingbin; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)High-turnover environments have been extensively studied with the bulk of the literature focusing on the negative effects on business operations.[1] They present challenges to the resilience of the organization while also limiting the potential profitability from consistently having to spend time training new staff. Furthermore, in manufacturing environments inexperienced staff are prone to mistakes and uncertainty, which can lead to increases in scrap materials and lower production rates due to a lack of mastery of the process. From an organizational standpoint a high-turnover environment presents an unmitigated risk to the organization from the continuous loss of institutional knowledge. This loss can present challenges to the organization in numerous ways, such as capital equipment that no longer has staff qualified or experienced enough to use it leading to costly retraining by the manufacturer or increased risk of a catastrophic failure resulting in damage to the equipment and or injury to the staff. Furthermore, the loss of institutional history leads to the loss of why operations are performed a certain way. As the common saying goes, “those who forget history are bound to repeat it.” which can lead to substantial costs for the organization while old solutions that were previously rejected due to lack of merit are constantly rehashed due to a lack of understanding of how the organization arrived at its current policies. This thesis presents a novel framework to mitigate the potential loss of institutional knowledge via a multifaceted approach. To achieve this a specific topic was identified and used to frame questions that guided the research. Mitigation of the negative impacts of high-turnover in manufacturing environments with a specific focus on the optimization of training programs. This topic led to the formulation of the following research questions. What steps can be taken to reduce the chance of lost institutional knowledge in a high-turnover environment? What steps can be taken to reduce the time needed to train a high performing replacement employee, while maintaining strict performance and safety standards? What steps should be taken to improve the accuracy of budgetary projections? What steps need to be taken to enable accurate analysis of potential future investment opportunities in a training program. The answers to the above research questions are compiled and presented with the aim to provide professionals, who are responsible for training programs in high-turnover environments that require a high organizational reliability, with a framework and analysis toolset that will enable data-driven decision making regarding the program. Additionally this thesis provides a framework for addressing the continuous risk of loss of institutional knowledge. When contrasted with a standard training model, where a trainee is presented with new material and then tested for retention before moving to the next topic, the proposed model implements a schema that can be rapidly iterated upon and improved until the desired performance outcome is achieved, while increasing the potential accuracy of budgetary estimation by as much as 57%. Throughout the process, decision makers will have insight into the long term effects of their potential actions by way of running simulations that give insight into not only the expected steady-state cost of a program but also the rough volume of trainees required to achieve that steady-state.Item Root Cause Analysis Of Adverse Events Using A Human Reliability Analysis Approach(2022) Johnson, David Michael; Vaughn-Cooke, Monifa; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Large scale analysis of adverse event data is challenging due to the unstructured nature of event reporting and narrative textual data in adverse event repositories. This issue is further complicated for human error adverse events, which are routinely treated as a root cause instead of as initiating events in a causal chain. Human error events are commonly misunderstood and underreported, which hinders the analysis of trends and the identification of risk mitigation strategies across industries. Currently, the prevailing means of human error investigation is the analysis of accident and incident data which are not designed around a framework of human cognition or psychomotor function. Existing approaches lack a theoretical foundation with sufficient cognitive granularity to identify root causes of human error. This research provides a cognitive task decomposition to standardize the investigation, reporting, and analysis of human error adverse event data in narrative textual form. The proposed method includes a qualitative structure to answer six questions (when, who, what, where, how, why) that are critical to comprehensively understand the events surrounding human error. This process is accomplished in five main stages: (1) Develop guidelines for a cognitively-driven adverse event investigation; (2) Perform a baseline cognitive task analysis (when) to document relevant stakeholders (who), products or processes (what), and environments (where) based on a taxonomy of cognitive and psychomotor function; (3) Identify deviations for the baseline task analysis in the form of unsafe acts (how) using a human error classification; (4) and Develop a root cause mapping to identify the performance shaping factors (PSFs) (why) for each unsafe act. The outcome of the proposed method will advance the fields of risk analysis and regulatory science by providing a standardized and repeatable process to input and analyze human error in adverse event databases. The method provides a foundation for more effective human error trending and accident analysis at a greater level of cognitive granularity. Application of this method to adverse event risk mitigations can inform prospective strategies such as resource allocation and system design, with the ultimate long-term goal of reducing the human contribution to risk.Item EVENT-DRIVEN OPERATION OF DISTRIBUTED SYSTEMS WITH ARTIFICIAL INTELLIGENCE TECHNOLOGIES AND BEHAVIOR MODELING(2022) Montezzo Coelho, Maria Eduarda; Austin, Mark A; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation aims to enhance decision making in urban settings by integrating artificial intelligence technologies with distributed behavior modeling. Today’s civil engineering systems are far more heterogeneous than their predecessors and may be connected to other types of systems in completely new ways, making the task of system design, analysis and integration of multi-disciplinary concerns much more difficult than in the past. These challenges can be addressed by combining machine learning formalisms and semantic model representations of urban systems, that work side-by-side in collecting data, identifying events, and managing city operations in real-time. We exercise the proposed approach on a problem involving anomaly detection in an urbanwater distribution system and a metrorail system.Item INTRODUCING MEETING LOCATIONS IN TAXI-SHARING SYSTEMS(2021) Aliari, Sanaz; Haghani, Ali A.H.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Taxi-sharing is admittedly a promising solution to reduce travel costs and mitigate congestion. However, sharing a ride with other passengers may impose long detours. Taxi-sharing can be more beneficial if the vehicle miles traveled for the detours can be reduced when serving additional passengers. In this study, we propose incorporating alternative meeting points in taxi-sharing routing to further boost efficiency by eliminating unnecessary detours and improving the chances of passengers being matched. Unlike traditional taxi-sharing systems, passengers are not necessarily picked up at their original location in the proposed system. Instead, they are serviced within a walkable distance of their origin (meeting points). This can be especially beneficial in heavily congested road networks in dense urban areas.There are several challenges in the real-world implementation of a ride-sharing system with alternative pick-up points in a dense high-demand network. The first challenge is to find appropriate passenger matches that may share a ride. The second challenge is to effectively find a set of specific locations as the potential alternative pick-up points that are likely to reduce total travel times. Once possible matches and the corresponding set of candidate pickup points are selected, the last challenge is to obtain optimal/near-optimal routes to satisfy the passengers’ demand with a reasonable computational time. In this study, we formulate a mixed integer programming model for the ride-sharing problem with alternative pick-up points and introduce strategies to cope with these challenges in a real-world setting. We use the New York City taxi demand data that may sometimes have hundreds of demands per minute in a relatively small geographical area. We first introduce a decomposition procedure to break down such a large-scale problem into smaller subproblems by pre-matching groups of passengers that are more likely to share a ride. We then create an initial set of candidate locations for all pick-up points in each group. Then, different strategies are proposed to reduce the set of candidate locations into smaller subsets, including alternative locations with higher potentials of constructing better routes. The experimental results show that incorporating alternative pick-up points results in significant savings in total travel times, total wait times, and the number of vehicles used compared to a baseline ride-sharing system that picks up each passenger exactly from their requested location. Finally, given the high computational cost of the optimization problem, we propose a genetic algorithm to find near-optimal solutions for the formulated problem, and we show that the proposed algorithm achieves solutions that are very close to the optimal solutions, with significantly lower computational times. We design specific operations to implement basic components of the genetic algorithm in the context of our algorithm. This includes strategies to diversify and create random sets of feasible solutions (individuals), select the fittest individuals in each generation, and create new generations through mutations and ordered cross-over such that the new individuals can inherit from their parents while still representing a feasible solution.Item FLIGHT ARRIVAL SCHEDULING MODELS FOR INCORPORATING COLLABORATIVE DECISION-MAKING CONCEPTS INTO TIME-BASED FLOW MANAGEMENT(2021) HAO, YEMING; Lovell, David J.; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Time-based air traffic flow management balances demand versus capacity at facilities by assigning Controlled Times of Arrival (CTAs) to incoming flights. However, existing systems do not differentiate delay costs among different aircraft and do not take user preferences into consideration. From a business perspective, it is essential to understand user preferences and to allow users to engage in decision-making. This dissertation presents results of simulations of strategies to incorporate business-driven airline preferences into these air traffic flow management systems following a Collaborative Decision-Making paradigm. We evaluate optimization models and heuristics to assign CTAs based on user-provided information and priority preferences in a way that minimizes the total CTA delay cost. Potential savings were quantified by comparing the results with the default first-come-first-served (FCFS) scheme. Monte Carlo simulations are conducted using historical flights data under a variety of realistic scenarios. Results show that our proposed heuristic, 2OptSwap, could reduce CTA delay cost between 20% and 30% relative to the FCFS baseline scheme, maintaining an average of 5.9% gap compared to the optimal solution. It is also shown that the heuristic could potentially realize the same level of cost savings regardless of whether the incoming flights are behind their schedules or not. Additional findings include that by starting the flow management further away, we could achieve more delay cost savings. The environment in the air space (such as the wind and the facility capacity) is constantly changing. Therefore, a rolling horizon approach was integrated into this air traffic flow management system. This approach allows the system to incorporate the most recent information at each epoch and solve the problem in a dynamic fashion. Real-time fairness metrics and adjustment methods are defined such that performance measurements in the previous epochs can be used for adjustments in future decision-making. Simulation results show that these fairness adjustments can help achieve a fairer benefits distribution among carriers and achieve a win-win solution.Item Dynamic Repositioning For Bikesharing Systems(2020) Roshan Zamir, Kiana; Haghani, Ali; Civil Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Bikesharing systems’ popularity has continuously been rising during the past years due to technological advancements. Managing and maintaining these emerging systems are indispensable parts of these systems and are necessary for their sustainable growth and successful implementation. One of the challenges that operators of these systems are facing is the uneven distribution of bikes due to users’ activities. These imbalances in the system can result in a lack of bikes or docks and consequently cause user dissatisfaction. A dynamic repositioning model that integrates prediction and routing is proposed to address this challenge. This operational model includes prediction, optimization, and simulation modules and can assist the operators of these systems in maintaining an effective system during peak periods with less number of unmet demands. It also can provide insights for planners by preparing development plans with the ultimate goal of more efficient systems. Developing a reliable prediction module that has the ability to predict future station-level demands can help system operators cope with the rebalancing needs more effectively. In this research, we utilize the expressive power of neural networks for predicting station-level demands (number of pick-ups and drop-offs) of bikeshare systems over multiple future time intervals. We examine the possibility of improving predictions by taking into account new sources of information about these systems, namely membership type and status of stations. A mathematical formulation is then developed for repositioning the bikes in the system with the goal of minimizing the number of unmet demands. The proposed module is a dynamic multi-period model with a rolling horizon which accounts for demands in the future time intervals. The performance of the optimization module and its assumptions are evaluated using discrete event simulation. Also, a three-step heuristic method is developed for solving large-size problems in a reasonable time. Finally, the integrated model is tested on several case studies from Capital Bikeshare, the District of Columbia’s bikeshare program.Item COST-EFFECTIVE PROGNOSTICS AND HEALTH MONITORING OF LOCALLY DAMAGED PIPELINES WITH HIGH CONFIDENCE LEVEL(2020) Aria, Amin; Modarres, Mohammad; Azarm, Shapour; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Localized pipeline damages, caused by degradation processes such as corrosion, are prominent, can result in pipeline failure and are expensive to monitor. To prevent pipeline failure, many Prognostics and Health Monitoring (PHM) approaches have been developed in which sensor network for online, and human inspection for offline data gathering are separately used. In this dissertation, a two-level (segment- and integrated-level) PHM approach for locally damaged pipelines is proposed where both of these degradation data gathering schemes (i.e., detection methods) are considered simultaneously. The segment-level approach, in which the damage behavior is considered to be uniform, consists of a static and a dynamic phase. In the static phase, a new optimization problem for the health monitoring layout design of locally damaged pipelines is formulated. The solution to this problem is an optimal configuration (or layout) of degradation detection methods with a minimized health monitoring cost and a maximized likelihood of damage detection. In the dynamic phase, considering the optimal layout, an online fusion of high-frequency sensors data and low-frequency inspection information is conducted to estimate and then update the pipeline’s Remaining Useful Life (RUL) estimate. Subsequently, the segment-level optimization formulation is modified to improve its scalability and facilitate updating layouts considering the online RUL estimates. Finally, at the integrated-level, the modified segment-level approach is used along with Stochastic Dynamic Programming (SDP) to produce an optimal set of layouts for a long pipeline consisting of multiple segments with different damage behavior. Experimental data and several notional examples are used to demonstrate the performance of the proposed approaches. Synthetically generated damage data are used in two examples to demonstrate that the proposed segment-level layout optimization approach results in a more robust solution compared to single detection approaches and deterministic methods. For the dynamic segment-level phase, acoustic emission sensor signals and microscopic images from a set of fatigue crack experiments are considered to show that combining sensor- and image-based damage size estimates leads to accuracy improvements in RUL estimation. Lastly, using synthetically generated damage data for three hypothetical pipeline segments, it is shown that the constructed integrated-level approach provides an optimal set of layouts for several pipeline segments.