Matching Items (28)
Description疫情期间，人们与社区发生了高频链接。流调、核酸、求助，社区工作者无时不刻出现在人们的生活中。社区作为中国基层治理的基础单元，政府需要上而下到社区执行防控工作，百姓则自下而上需要社区提供服务和帮助、向上反映各类居民诉求。伴随着中国的城镇化进程，全中国有9万个城市社区、400万社区工作者，社区参与主体从居委会、物业到民非组织，不断地进化。在人们对未来生活需求不断提高的时侯，社区服务提供者协同社区各主体，实现数字社区可持续发展？本研究围绕“社区服务提供者如何在后疫情时代构建可持续的社区协同机制？”这一研究问题，首先，对数字社区可持续发展和数字社区协同机制相关研究进行梳理和回顾；第二，将以数字社区建设和协同治理为出发点，以七彩集团为研究样本，分析其实际运营的滨江缤纷未来社区和萧山瓜沥未来社区案例，归纳总结其协同治理的具体措施；第三，结合理论规范分析，提出“数字社区协同机制-协同绩效”的理论框架和作用边界；第四，运用问卷调查结合因子分析的方法，对这些具体措施能够实际提高企业绩效进行问卷发放和数据验证。
本研究得到以下三个主要结论：（1）数字社区中可持续性发展协同治理机制是社区运营主体利用数字化技术对社区内参与提供和使用服务的治理活动进行约束、激励、引导和管理的一系列制度安排，同时包括政策治理、社区文化和市场共建三种不同作用机制。（2）数字社区中，治理政策机制仅在社团主体中作用显著；社区文化除了在社区物业中作用有限，在其他所有主体中均发挥重要作用；市场共建则是在社团、物业和居民业主中发挥作用。（3）协同机制通过政策治理、社区文化、市场共建影响社区内不同主体的感知和行为，而数字技术作用一种新兴的支撑性技术能够对上述作用产生不同的增强作用，进而促进协同绩效提升。
本研究通过聚焦于社区运营六方主体的角色分工、各自诉求，进一步讨论如何应用最新数字经济和技术来找到可持续发展的协同机制，为后疫情时代中国社区的良性发展找到解决方案。
ContributorsXu, Xiaowei (Author) / Shao, Benjamin (Thesis advisor) / Zhang, Anmin (Thesis advisor) / Gu, Bin (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2023
Description
In this dissertation, a cyber-physical system called MIDAS (Managing Interacting Demand And Supply) has been developed, where the “supply” refers to the transportation infrastructure including traffic controls while the “demand” refers to its dynamic traffic loads. The strength of MIDAS lies in its ability to proactively control and manage mixed vehicular traffic, having various levels of autonomy, through traffic intersections. Using real-time traffic control algorithms MIDAS minimizes wait times, congestion, and travel times on existing roadways. For traffic engineers, efficient control of complicated traffic movements used at diamond interchanges (DI), which interface streets with freeways, is challenging for normal human driven vehicular traffic, let alone for communicationally-connected vehicles (CVs) due to stochastic demand and uncertainties. This dissertation first develops a proactive traffic control algorithm, MIDAS, using forward-recursion dynamic programming (DP), for scheduling large set of traffic movements of non-connected vehicles and CVs at the DIs, over a finite-time horizon. MIDAS captures measurements from fixed detectors and captures Lagrangian measurements from CVs, to estimate link travel times, arrival times and turning movements. Simulation study shows MIDAS’ outperforms (a) a current optimal state-of-art optimal fixed-cycle time control scheme, and (b) a state-of-art traffic adaptive cycle-free scheme.
Subsequently, this dissertation addresses the challenges of improving the road capacity by platooning fully autonomous vehicles (AVs), resulting in smaller headways and greater road utilization. With the MIDAS AI (Autonomous Intersection) control, an effective platooning strategy is developed, and optimal release sequence of AVs is determined using a new forward-recursive DP that minimizes the time-loss delays of AVs. MIDAS AI evaluates the DP decisions every second and communicates optimal actions to the AVs.
Although MIDAS AI’s exact DP achieves optimal solution in almost real-time compared to other exact algorithms, it suffers from scalability. To address this challenge, the dissertation then develops MIDAS RAIC (Reinforced Autonomous Intersection Control), a deep reinforcement learning based real-time dynamic traffic control system for AVs at an intersection. Simulation results show the proposed deep Q-learning architecture trains MIDAS RAIC to learn a near-optimal policy that minimizes the total cumulative time loss delay and performs nearly as well as the MIDAS AI.
ContributorsPotluri, Viswanath (Author) / Mirchandani, Pitu (Thesis advisor) / Ju, Feng (Committee member) / Zhou, Xuesong (Committee member) / Sefair, Jorge (Committee member) / Arizona State University (Publisher)
Created2023
Description近几年来，科技创新已经成为高新技术企业发展的重要动力源泉，如何提高企业的创新绩效，增强企业的竞争力，一直以来都是广大学者讨论的热点。高管是公司的核心，也是公司战略决策机构的主体，同时还是企业与内外部环境进行联系的重要纽带。高管的学习力会影响到高管群体职能的发挥，更会影响到企业的决策，这对企业的创新绩效有着母庸置疑的影响。目前，对于高管个人学习力的研究文献较少，关于高管学习力对创新绩效的作用机制的研究更是匮乏。高管学习力与创新绩效是怎么样的关系，哪些要素影响到了这两者的关系，都是需要进行深入挖掘的问题。本文深入探究了高管学习力与创新绩效的关系，并详细分析了高管学习力对创新绩效的影响路径和影响机制。主要的研究内容如下：
本文根据基础理论，分析了学习的转化机制与企业动态能力之间的关系，建立起本文的研究模型。利用陈国权的个人学习力模型，将高管学习力分为9个维度，同时确定组织学习为中介变量，调节变量确定为主动性人格与环境动态性。基于研究模型和现有的文献内容，本文提出了研究假设。而后通过问卷的方式收集到相关数据，通过对数据进行信度分析、效度分析和相关性分析后，确定了数据的可靠性。最后，通过描述性分析，回归分析的方法对研究假设进行了验证。
最终本文得出了以下结论：
（1）高管学习力能够正向影响到创新绩效。（2）高管学习力能够正向影响到组织学习。（3）组织学习能够正向影响到创新绩效。（4）在高管学习力与创新绩效的关系中，组织学习起到中介作用。（5）在高管学习力与组织学习的关系中，主动性人格起到正向调节作用。（6）在组织学习与创新绩效的关系中，环境动态性起到正向调节作用。
基于上述研究成果，本文对高管学习力和创新绩效之间的关系探究做了一次有益的尝试，为广大的企业和学者提供了一个崭新的思路和视角。同时也丰富了关于高管学习力和企业创新绩效的相关研究内容。
ContributorsTu, Yihua (Author) / Zhu, David (Thesis advisor) / Yan, Hong (Thesis advisor) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2023
Description
Sequential event prediction or sequential pattern mining is a well-studied topic in the literature. There are a lot of real-world scenarios where the data is released sequentially. People believe that there exist repetitive patterns of event sequences so that the future events can be predicted. For example, many companies build their recommender system to predict the next possible product for the users according to their purchase history. The healthcare system discovers the relationships among patients’ sequential symptoms to mitigate the adverse effect of a treatment (drugs or surgery). Modern engineering systems like aviation/distributed computing/energy systems diagnosed failure event logs and took prompt actions to avoid disaster when a similar failure pattern occurs. In this dissertation, I specifically focus on building a scalable algorithm for event prediction and extraction in the aviation domain. Understanding the accident event is always the major concern of the safety issue in the aviation system. A flight accident is often caused by a sequence of failure events. Accurate modeling of the failure event sequence and how it leads to the final accident is important for aviation safety. This work aims to study the relationship of the failure event sequence and evaluate the risk of the final accident according to these failure events. There are three major challenges I am trying to deal with. (1) Modeling Sequential Events with Hierarchical Structure: I aim to improve the prediction accuracy by taking advantage of the multi-level or hierarchical representation of these rare events. Specifically, I proposed to build a sequential Encoder-Decoder framework with a hierarchical embedding representation of the events. (2) Lack of high-quality and consistent event log data: In order to acquire more accurate event data from aviation accident reports, I convert the problem into a multi-label classification. An attention-based Bidirectional Encoder Representations from Transformers model is developed to achieve good performance and interpretability. (3) Ontology-based event extraction: In order to extract detailed events, I proposed to solve the problem as a hierarchical classification task. I improve the model performance by incorporating event ontology. By solving these three challenges, I provide a framework to extract events from narrative reports and estimate the risk level of aviation accidents through event sequence modeling.
ContributorsZhao, Xinyu (Author) / Yan, Hao (Thesis advisor) / Liu, Yongming (Committee member) / Ju, Feng (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Additive manufacturing consists of successive fabrication of materials layer upon layer to manufacture three-dimensional items. Several key problems such as poor quality of finished products and excessive operational costs are yet to be addressed before it becomes widely applicable in the industry. Retroactive/offline actions such as post-manufacturing inspections for defect detection in finished products are not only extremely expensive and ineffective but are also incapable of issuing corrective action signals during the building span. In-situ monitoring and optimal control methods, on the other hand, can provide viable alternatives to aid with the online detection of anomalies and control the process. Nevertheless, the complexity of process assumptions, unique structure of collected data, and high-frequency data acquisition rate severely deteriorates the performance of traditional and parametric control and process monitoring approaches. Out of diverse categories of additive manufacturing, Large-Scale Additive Manufacturing (LSAM) by material extrusion and Laser Powder Bed Fusion (LPBF) suffer the most due to their more advanced technologies and are therefore the subjects of study in this work. In LSAM, the geometry of large parts can impact the heat dissipation and lead to large thermal gradients between distance locations on the surface. The surface's temperature profile is captured by an infrared thermal camera and translated to a non-linear regression model to formulate the surface cooling dynamics. The surface temperature prediction methodology is then combined into an optimization model with probabilistic constraints for real-time layer time and material flow control. On-axis optical high-speed cameras can capture streams of melt pool images of laser-powder interaction in real-time during the process. Model-agnostic deep learning methods offer a great deal of flexibility when facing such unstructured big data and thus are appealing alternatives to their physical-related and regression-based modeling counterparts. A configuration of Convolutional Long-Short Term Memory (ConvLSTM) auto-encoder is proposed to learn a deep spatio-temporal representation from sequences of melt pool images collected from experimental builds. The unfolded bottleneck tensors are then further mined to construct a high accuracy and low false alarm rate anomaly detection and monitoring procedure.
ContributorsFathizadan, Sepehr (Author) / Ju, Feng (Thesis advisor) / Wu, Teresa (Committee member) / Lu, Yan (Committee member) / Iquebal, Ashif (Committee member) / Arizona State University (Publisher)
Created2022
Description
Two-sided online platforms are typically plagued by hidden information (adverse selection) and hidden actions (moral hazard), limiting market efficiency. Under the context of the increasingly popular online labor contracting platforms, this dissertation investigates whether and how IT-enabled monitoring systems can mitigate moral hazard and reshape the labor demand and supply by providing detailed information about workers’ effort. In the first chapter, I propose and demonstrate that monitoring records can substitute for reputation signals such that they attract more qualified inexperienced workers to enter the marketplace. Specifically, only the effort-related reputation information is substituted by monitoring but the capability-related reputation information. In line with this, monitoring can lower the entry barrier for inexperienced workers on platforms. In the second chapter, I investigate if there is home bias for local workers when employers make the hiring decisions. I further show the existence of home bias from employers and it is primarily driven by statistical inference instead of personal “taste”. In the last chapter, I examine if females tend to have a stronger avoidance of monitoring than males. With the combination of the observational data and experimental data, I find that there is a gender difference in avoidance of monitoring and the introduction of the monitoring system increases the gender wage gap due to genders differences in such willingness-to-pay for the avoidance of monitoring. These three studies jointly contribute to the literature on the online platforms, gig economy and agency theory by elucidating the critical role of IT-enabled monitoring.
ContributorsLiang, Chen, Ph.D (Author) / Gu, Bin (Thesis advisor) / Hong, Yili (Thesis advisor) / Chen, Peiyu (Committee member) / Arizona State University (Publisher)
Created2019
Description
The use of Red Blood Cells (RBCs) is a pillar of modern health care. Annually, the lives of hundreds of thousands of patients are saved through ready access to safe, fresh, blood-type compatible RBCs. Worldwide, hospitals have the common goal to better utilize available blood units by maximizing patients served and reducing blood wastage. Managing blood is challenging because blood is perishable, its supply is stochastic and its demand pattern is highly uncertain. Additionally, RBCs are typed and patient compatibility is required.
This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served.
The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction.
Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza.
Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance.
This research focuses on improving blood inventory management at the hospital level. It explores the importance of hospital characteristics, such as demand rate and blood-type distribution in supply and demand, for improving RBC inventory management. Available inventory models make simplifying assumptions; they tend to be general and do not utilize available data that could improve blood delivery. This dissertation develops useful and realistic models that incorporate data characterizing the hospital inventory position, distribution of blood types of donors and the population being served.
The dissertation contributions can be grouped into three areas. First, simulations are used to characterize the benefits of demand forecasting. In addition to forecast accuracy, it shows that characteristics such as forecast horizon, the age of replenishment units, and the percentage of demand that is forecastable influence the benefits resulting from demand variability reduction.
Second, it develops Markov decision models for improved allocation policies under emergency conditions, where only the units on the shelf are available for dispensing. In this situation the RBC perishability has no impact due to the short timeline for decision making. Improved location-specific policies are demonstrated via simulation models for two emergency event types: mass casualty events and pandemic influenza.
Third, improved allocation policies under normal conditions are found using Markov decision models that incorporate temporal dynamics. In this case, hospitals receive replenishment and units age and outdate. The models are solved using Approximate Dynamic Programming with model-free approximate policy iteration, using machine learning algorithms to approximate value or policy functions. These are the first stock- and age-dependent allocation policies that engage substitution between blood type groups to improve inventory performance.
ContributorsDumkrieger, Gina (Author) / Mirchandani, Pitu B. (Thesis advisor) / Fowler, John (Committee member) / Wu, Teresa (Committee member) / Ju, Feng (Committee member) / Arizona State University (Publisher)
Created2020
Description
Recent advances in manufacturing system, such as advanced embedded sensing, big data analytics and IoT and robotics, are promising a paradigm shift in the manufacturing industry towards smart manufacturing systems. Typically, real-time data is available in many industries, such as automotive, semiconductor, and food production, which can reflect the machine conditions and production system’s operation performance. However, a major research gap still exists in terms of how to utilize these real-time data information to evaluate and predict production system performance and to further facilitate timely decision making and production control on the factory floor. To tackle these challenges, this dissertation takes on an integrated analytical approach by hybridizing data analytics, stochastic modeling and decision making under uncertainty methodology to solve practical manufacturing problems.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
Specifically, in this research, the machine degradation process is considered. It has been shown that machines working at different operating states may break down in different probabilistic manners. In addition, machines working in worse operating stage are more likely to fail, thus causing more frequent down period and reducing the system throughput. However, there is still a lack of analytical methods to quantify the potential impact of machine condition degradation on the overall system performance to facilitate operation decision making on the factory floor. To address these issues, this dissertation considers a serial production line with finite buffers and multiple machines following Markovian degradation process. An integrated model based on the aggregation method is built to quantify the overall system performance and its interactions with machine condition process. Moreover, system properties are investigated to analyze the influence of system parameters on system performance. In addition, three types of bottlenecks are defined and their corresponding indicators are derived to provide guidelines on improving system performance. These methods provide quantitative tools for modeling, analyzing, and improving manufacturing systems with the coupling between machine condition degradation and productivity given the real-time signals.
ContributorsKang, Yunyi (Author) / Ju, Feng (Thesis advisor) / Pedrielli, Giulia (Committee member) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Arizona State University (Publisher)
Created2020
Description
Modern manufacturing systems are part of a complex supply chain where customer preferences are constantly evolving. The rapidly evolving market demands manufacturing organizations to be increasingly agile and flexible. Medium term capacity planning for manufacturing systems employ queueing network models based on stationary demand assumptions. However, these stationary demand assumptions are not very practical for rapidly evolving supply chains. Nonstationary demand processes provide a reasonable framework to capture the time-varying nature of modern markets. The analysis of queues and queueing networks with time-varying parameters is mathematically intractable. In this dissertation, heuristics which draw upon existing steady state queueing results are proposed to provide computationally efficient approximations for dynamic multi-product manufacturing systems modeled as time-varying queueing networks with multiple customer classes (product types). This dissertation addresses the problem of performance evaluation of such manufacturing systems.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
This dissertation considers the two key aspects of dynamic multi-product manufacturing systems - namely, performance evaluation and optimal server resource allocation. First, the performance evaluation of systems with infinite queueing room and a first-come first-serve service paradigm is considered. Second, systems with finite queueing room and priorities between product types are considered. Finally, the optimal server allocation problem is addressed in the context of dynamic multi-product manufacturing systems. The performance estimates developed in the earlier part of the dissertation are leveraged in a simulated annealing algorithm framework to obtain server resource allocations.
ContributorsJampani Hanumantha, Girish (Author) / Askin, Ronald (Thesis advisor) / Ju, Feng (Committee member) / Yan, Hao (Committee member) / Mirchandani, Pitu (Committee member) / Arizona State University (Publisher)
Created2020
Description
Degradation process, as a course of progressive deterioration, commonly exists on many engineering systems. Since most failure mechanisms of these systems can be traced to the underlying degradation process, utilizing degradation data for reliability prediction is much needed. In industries, accelerated degradation tests (ADTs) are widely used to obtain timely reliability information of the system under test. This dissertation develops methodologies for the ADT data modeling and analysis.
In the first part of this dissertation, ADT is introduced along with three major challenges in the ADT data analysis – modeling framework, inference method, and the need of analyzing multi-dimensional processes. To overcome these challenges, in the second part, a hierarchical approach, that leads to a nonlinear mixed-effects regression model, to modeling a univariate degradation process is developed. With this modeling framework, the issues of ignoring uncertainties in both data analysis and lifetime prediction, as presented by an International Standard Organization (ISO) standard, are resolved. In the third part, an approach to modeling a bivariate degradation process is addressed. It is developed using the copula theory that brings the benefits of both model flexibility and inference convenience. This approach is provided with an efficient Bayesian method for reliability evaluation. In the last part, an extension to a multivariate modeling framework is developed. Three fundamental copula classes are applied to model the complex dependence structure among correlated degradation processes. The advantages of the proposed modeling framework and the effect of ignoring tail dependence are demonstrated through simulation studies. The applications of the copula-based multivariate degradation models on both system reliability evaluation and remaining useful life prediction are provided.
In summary, this dissertation studies and explores the use of statistical methods in analyzing ADT data. All proposed methodologies are demonstrated by case studies.
In the first part of this dissertation, ADT is introduced along with three major challenges in the ADT data analysis – modeling framework, inference method, and the need of analyzing multi-dimensional processes. To overcome these challenges, in the second part, a hierarchical approach, that leads to a nonlinear mixed-effects regression model, to modeling a univariate degradation process is developed. With this modeling framework, the issues of ignoring uncertainties in both data analysis and lifetime prediction, as presented by an International Standard Organization (ISO) standard, are resolved. In the third part, an approach to modeling a bivariate degradation process is addressed. It is developed using the copula theory that brings the benefits of both model flexibility and inference convenience. This approach is provided with an efficient Bayesian method for reliability evaluation. In the last part, an extension to a multivariate modeling framework is developed. Three fundamental copula classes are applied to model the complex dependence structure among correlated degradation processes. The advantages of the proposed modeling framework and the effect of ignoring tail dependence are demonstrated through simulation studies. The applications of the copula-based multivariate degradation models on both system reliability evaluation and remaining useful life prediction are provided.
In summary, this dissertation studies and explores the use of statistical methods in analyzing ADT data. All proposed methodologies are demonstrated by case studies.
ContributorsFANG, GUANQI (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Ju, Feng (Committee member) / Hong, Yili (Committee member) / Arizona State University (Publisher)
Created2020