Reliable design of interdependent service facility systems under correlated disruption risks

Facility location decisions lie at the center of planning many infrastructure systems. In many practice, public agencies (e.g., governments) and private companies (e.g., retailers) need to locate facilities to serve spatially distributed demands. For example, governments locate public facilities, e.g., hospitals, schools, fire stations, to provide public services; retail companies determine the locations of their warehouses and stores to provide business. The design of such facility systems involves considerations of investment of facility construction and transportation cost of serving demands, so as to maximize the system operational efficiency and profit. Recently, devastating infrastructure damages observed in real world show that infrastructure facilities may be subject to disruptions that compromise individual facility functionality as well as overall system performance. This emphasizes the necessity of taking facility disruptions into consideration during planning to balance between system efficiency and reliability. Furthermore, facility systems often exhibit complex interdependence when: (1) facilities are spatially correlated due to physical connections/interrelations, and (2) facilities provide combinatorial service under cooperation, competition and/or restrictions. These further complicate the facility location design. Therefore, facility location models need to be extended to tackle all these challenges and design a reliable interdependent facility system. This dissertation aims at investigating several important and challenging topics in the reliable facility location context, including facility correlations, facility combinations, and facility districting. The main work of this PhD research consist of: (1) establishing a new systematic methodological framework based on supporting stations and quasi-probabilities to describe and decompose facility correlations into succinct mathematical representations, which allows compact mathematical formulations to be developed for planning facility locations under correlated facility disruptions; (2) expanding the modeling framework to allow facilities to provide combinatorial service; e.g., in the context of sensor deployment problems, where sensors work in combinations to provide positioning/surveillance service via trilateration procedure; and (3) incorporating the concepts of spatial districting into the reliable facility location context, with the criteria of spatial contiguity, compactness, and demand balance being ensured. First, in many real-world facility systems, facility disruptions exhibit spatial correlations, which have strong impacts on the system performance, but are difficult to be described with succinct mathematical models. We first investigate facility systems with correlations caused by facilities’ share of network access points (e.g., bridges, railway crossings), which are required to be passed through by customers to visit facilities. We incorporate these network access points and their probabilistic failures into a joint optimization framework. A layer of supporting stations are added to represent the network access points, and are connected to facilities to indicate their real-world relationships. We then develop a compact mixed-integer mathematical model to optimize the facility location and customer assignment decisions. Lagrangian relaxation based algorithms are designed to effectively solve the model. Multiple case studies are constructed to test the model and algorithm, and to demonstrate their performance and applicability. Next, when there exists no real access points, facilities could also be correlated if they are exposed to shared hazards. We develop a virtual station structure framework to decompose these types of facility correlations. First, we define three probabilistic representations of correlated facility disruptions (i.e., with scenario, marginal, and conditional probabilities), derive pairwise transformations between them, and theoretically prove their equivalence. We then provide detailed formulas to transform these probabilistic representations into an equivalent virtual station structure, which enables the decomposition of any correlated facility disruptions into a compact network structure with only independent failures, and helps avoid enumerating an exponential number of disruption scenarios. Based on the augmented system, we propose a compact mixed-integer optimization program, and design several customized solution approaches based on Lagrangian relaxation to efficiently solve the model. We demonstrate our methodology on a series of numerical examples involving different correlation patterns and varying network and parameter settings. We then apply the reliable location modeling framework to sensor deployment problems, where multiple sensors work in combinations to provide combinatorial coverage service to customers via trilateration procedure. Since various sensor combinations may share common sensors, one combination is typically interrelated with some other combinations, which leads to internal correlations among the functionality of sensors and sensor combinations. We address the problem of where to deploy sensors, which sensor combinations are selected to use, and in what sequence and probability to use these combinations in case of disruptions. A compact mixed-integer mathematical model is developed to formulate the problem, by combining and extending the ideas of assigning back-up sensors and correlation decomposition via supporting stations. A customized solution algorithm based on Lagrangian relaxation and branch-and-bound is developed, together with several embedded approximation subroutines for solving subproblems. A series of numerical examples are investigated to illustrate the performance of the proposed methodology and to draw managerial insights. Finally, we develop an innovative reliable network districting framework to incorporate districting concepts into the reliable facility location context. Districting criteria including spatial contiguity, compactness, and demand balance are enforced for location design and extended in considerations of facility disruptions. The problem is modeled into a reliable network districting problem, in the form of a location-assignment based model. We develop customized solution approaches, including heuristics (i.e., constructive heuristic and neighborhood search) and set-cover based algorithms (e.g., district generation, lower bound estimation) to provide near-optimum solution with optimality gap. A series of hypothetical cases and an empirical full-scale application are presented to demonstrate the performance of our methodology for different network and parameter settings

Positioning, Planning and Operation of Emergency Response Resources and Coordination between Jurisdictions

Railroad related rail incidents, particularly those involving hazardous material (hazmat), cause severe consequences and pose significant threats to safety, public health and the environment. Rail safety is a huge issue in Midwestern states such as Illinois, Wisconsin, and Minnesota. This project aims at strategically positioning and allocating emergency responders and resources in anticipation of potential accidents in a region that may be impacted by rail incidents. Mathematical models and solution techniques are developed to enable systematic analysis of the emergency response system associated with railroad incidents; e.g., to strategically position and allocate emergency responders and resources in anticipation of potential accidents along spatially distributed railroad networks. We consider the added complexity due to vulnerability of the emergency response system itself, such as the risk of disruptions to the transportation network for first-responders (e.g., blockage of railroad crossings). The outcomes from these tasks will provide fundamental understanding, operational guidelines, and practical tools to policy makers (e.g., federal and state agencies) to induce socio-economically favorable system that support safe and efficient railroad industry operations

Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units Detection

This paper presents our Facial Action Units (AUs) recognition submission to the fifth Affective Behavior Analysis in-the-wild Competition (ABAW). Our approach consists of three main modules: (i) a pre-trained facial representation encoder which produce a strong facial representation from each input face image in the input sequence; (ii) an AU-specific feature generator that specifically learns a set of AU features from each facial representation; and (iii) a spatio-temporal graph learning module that constructs a spatio-temporal graph representation. This graph representation describes AUs contained in all frames and predicts the occurrence of each AU based on both the modeled spatial information within the corresponding face and the learned temporal dynamics among frames. The experimental results show that our approach outperformed the baseline and the spatio-temporal graph representation learning allows our model to generate the best results among all ablated systems. Our model ranks at the 4th place in the AU recognition track at the 5th ABAW Competition

Current monitoring in nanochannels

CBET-1653767 - National Science FoundationAccepted manuscrip

Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning

Sound events in daily life carry rich information about the objective world. The composition of these sounds affects the mood of people in a soundscape. Most previous approaches only focus on classifying and detecting audio events and scenes, but may ignore their perceptual quality that may impact humans’ listening mood for the environment, e.g. annoyance. To this end, this paper proposes a novel hierarchical graph representation learning (HGRL) approach which links objective audio events (AE) with subjective annoyance ratings (AR) of the soundscape perceived by humans. The hierarchical graph consists of fine-grained event (fAE) embeddings with single-class event semantics, coarse-grained event (cAE) embeddings with multi-class event semantics, and AR embeddings. Experiments show the proposed HGRL successfully integrates AE with AR for AEC and ARP tasks, while coordinating the relations between cAE and fAE and further aligning the two different grains of AE information with the AR

Asset Specificity on the Intention of Farmers to Continue Land Recuperation: Based on the Perspective of Farmer Differentiation

Land recuperation is an important institutional guarantee for green agricultural development and an important measure to promote rural revitalization. Asset specificity is a crucial factor that affects farmers’ subsequent willingness to participate in land recuperation. Based on the perspective of farmer differentiation, this study uses survey data of 605 farmers in four counties of Gansu Province and employs the entropy method and the double-hurdle model to measure asset specificity and how it affects the subsequent willingness of different types of farmers to participate in land recuperation. The results show that: (1) farmers’ willingness to participate in land recuperation increases with the degree of their part-time occupations; (2) geographical location specificity has a significant negative effect on farmers’ intention and degree of subsequent land recuperations, and the impacts on non-farmers and II part-time farmers are significantly smaller than that on pure farmers and part-time farmers; (3) physical asset specificity has the most negligible influence on farmers’ subsequent willingness to participate; (4) human capital specificity has a significant negative impact on the intention and degree of land recuperation by farmers, and the effect is more significant for pure farmers than non-farmers; (5) factors such as land recuperation compensation satisfaction, land recuperation policy trust, social connection, and off-farm employment willingness promote the subsequent land recuperation willingness and degree of land recuperation of farmers, while the cultivated land area reduces the subsequent degree of participation in land recuperation

Joint Optimal Power Allocation and Relay Selection Scheme in Energy Harvesting Two-Way Relaying Network

In this paper, we propose a joint power allocation, time switching (TS) factor and relay selection scheme for an energy harvesting two-way relaying communication network (TWRN), where two transceivers exchange information with the help of a wireless-powered relay. By exploiting the TS architecture at the relay node, the relay node needs to use additional time slots for energy transmission, reducing the transmission rate. Thus, we propose a joint resource allocation algorithm to maximize the max-min bidirectional instantaneous information rate. To solve the original non-convex optimization problem, the objective function is decomposed into three sub-problems and solved sequentially. The closed-form solution of the transmit power of two sources and the optimal TS factor can be obtained by the information rate balancing technology and the proposed time allocation scheme, respectively. At last, the optimal relay node can be obtained. Simulation results show that the performance of the proposed algorithm is better than the traditional schemes and power-splitting (PS) scheme

Effects of land recuperation on farmers’ social capital: a Chinese field analysis

Social capital is an integral part of farmers’ life, which can be exogenously affected by land recuperation. Based on 1240 farmer field survey data in Gansu Province, this paper used the Logit model to analyse the influencing factors of farmers’ participation in land recuperation, and used the entropy method to measure social capital from the three dimensions of social network, social trust and social norms, and further used the propensity matching score method to measure the effect of land recuperation on farmers, and then compared the effects under different fixed ages and education groups. The following factors significantly affected farmers’ participation in land recuperation: age, years of education, migrant workers’ relationships with family and friends, relationship between migrant workers and friends and colleagues in the workplace, number of migrant workers away from home, cultivated land area, and family living standards. Land recuperation had the greatest promotion effect on farmer’ social network (163.9%), followed by social trust (28.0%) and social norm (11.3%). According to the results of group differences, land recuperation most significantly affected the social capital of farmers aged 45–55 years and household heads educated for 9–12 years compared to other age and education groups