Safety is a continually significant issue in the construction industry. The Occupation Safety and Health Administration as well as individual construction companies are constantly working on verifying that their selected safety plans have a positive effect on reduction of workplace injuries. Worker safety is a large concern for both the workers and employers in construction and the government also attempts to impose effective regulations concerning minimum safety requirements. There are many different methods for creating and implementing a safety plan, most notably the Construction Industry Institute's (CII) Zero Accidents Techniques (ZAT). This study will attempt to identify a relationship between the level of ZAT implementation and safety performance on industrial construction projects. This research also proposes that focusing efforts on certain ZAT elements over others will show different safety performance results. There are three findings in this study that can be used to assist safety professionals in designing efficient construction safety plans. The first is a significant log-log relationship that is identified between the DEA efficiency scores and Recordable Incident Rate (RIR). There is also a significant difference in safety performance found between the Light Industrial and Heavy Industrial sectors. Lastly, regression is used to show that the pre-construction and worker selection ZAT components can predict a better safety performance. / Master of Science

Linkages between Complex Adaptive Systems (CAS) thinking and efficiency analysis remain in their infancy. This research associates the basic building blocks of the CAS 'flocking' metaphor with the essential building block concepts of Data Envelopment Analysis (DEA). Within a proposed framework DEA "decision-making units" (DMUs) are represented as agents in the agent-based modeling (ABM) paradigm. Guided by simple rules, agent DMUs representing business units of a larger management system, 'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Analysis of the resulting patterns of behavior can provide policy insights that are both evidence-based and intuitive. This research introduces a consistent methodology that will be called here the Complex Adaptive Productive Efficiency Method (CAPEM) and employs it to bridge these domains. This research formalizes CAPEM mathematically and graphically. It then conducts experimentation employing using the resulting CAPEM simulation using data of a sample of electric power plants obtained from Rungsuriyawiboon and Stefanou (2003). Guided by rules, individual agent DMUs (power plants) representing business units of a larger management system,'align' with one another to achieve mutual protection/risk reduction and 'cohere' with the most efficient DMUs among them to achieve the greatest possible efficiency in the least possible time. Using a CAS ABM simulation, it is found that the flocking rules (alignment, cohesion and separation), taken individually and in selected combinations, increased the mean technical efficiency of the power plant population and conversely decreased the time to reach the frontier. It is found however that these effects were limited to a smaller than expected sub-set of these combinations of the flocking factors. Having been successful in finding even a limited sub-set of flocking rules that increased efficiency was sufficient to support the hypotheses and conclude that employing the flocking metaphor offers useful options to decision-makers for increasing the efficiency of management systems. / Ph. D.

Studying recurrent mobility perturbations, such as traffic congestions, is a major concern of engineers, planners, and authorities as they not only bring about delay and inconvenience but also have consequent negative impacts like greenhouse gas emission, increase in fuel consumption, or safety issues. In this dissertation, we proposed using the resilience concept, which has been commonly used for assessing the impact of extreme events and disturbances on the transportation system, for high-probability low impact (HPLI) events to (a) provide a performance assessment framework for transportation systems' response to traffic congestions, (b) investigate the role of transit modes in the resilience of urban roadways to congestion, and (c) study the impact of network topology on the resilience of roadways functionality performance. We proposed a multi-dimensional approach to characterize the resilience of urban transportation roadways for recurrent congestions. The resilience concept could provide an effective benchmark for comparative performance and identifying the behavior of the system in the discharging process in congestion. To this end, we used a Data Envelopment Analysis (DEA) approach to integrate multiple resilience-oriented attributes to estimate the efficiency (resilience) of the frontier in roadways. Our results from an empirical study on California highways through the PeMS data have shown the potential of the multi-dimensional approach in increasing information gain and differentiating between the severity of congestion across a transportation network. Leveraging this resilience-based characterization of recurrent disruptions, in the second study, we investigated the role of multi-modal resourcefulness of urban transportation systems, in terms of diversity and equity, on the resilience of roadways to daily-based congestions. We looked at the physical infrastructure availability and distribution (i.e. diversity) and accessibility and coverage to capture socio-economic factors (i.e. equity) to more comprehensively understand the role of resourcefulness in resilience. We conducted this investigation by using a GPS dataset of taxi trips in the Washington DC metropolitan area in 2017. Our results demonstrated the strong correlation of trips' resilience with transportation equity and to a lesser extent with transportation diversity. Furthermore, we learned the impact of equity and diversity can mostly be seen at the recovery stage of resilience. In the third study, we looked at another aspect of transportation supply in urban areas, spatial configuration, and topology. The goal of this study was to investigate the role of network topology and configuration on resilience to congestion. We used OSMnx, a toolkit for street network analysis based on the data from OpenStreetMap, to model and analyze the urban roadways network configurations. We further employed a multidimensional visualization strategy using radar charts to compare the topology of street networks on a single graphic. Leveraging the geometric descriptors of radar charts, we used the compactness and Jaccard Index to quantitatively compare the topology profiles. We use the same taxi trips dataset used in the second study to characterize resilience and identify the correlation with network topology. The results indicated a strong correlation between resilience and betweenness centrality, diameter, and Page Rank among other features of a transportation network. We further looked at the capacity of roadways as a common cause for the strong correlation between network features and resilience. We found that the strong correlation of link-related features such as diameter could be due to their role in capacity and have a common cause with resilience. / Doctor of Philosophy / Transportation infrastructure systems are among the most fundamental facilities and systems in urban areas due to the role they play in mobility, economy, and environmental sustainability. Due to this importance, it is crucial to ensure their resilience to regular disruptions such as traffic congestions as a priority for engineers and policymakers. The resilience of transportation systems has often been studied when disasters or extreme events occur. However, minor disturbances such as everyday operational traffic situations can also play an important part in reducing the efficiency of transportation systems and should be considered in the overall resilience of the systems. Current literature does not consider traffic performance from the lens of resilience despite its importance in evaluating the overall performance of roads. This research addresses this gap by proposing to leverage the concept of resilience for evaluation of roadways performance and identifying the role of urban characteristics in the enhancement of resilience. We first characterized resilience considering the performance of the roadways over time, ranging from the occurrence of disruptions to the time point when the system performance returns to a stable state. Through a case study on some of the major highways in the Los Angeles metropolitan area and by leveraging the data from the Performance Measurement System (PeMS), we have investigated how accounting for a proposed multi-dimensional approach for quantification of resilience could add value to the process of road network performance assessment and the corresponding decision-making. In the second and third parts of this dissertation, we looked at the urban infrastructure elements and how they affect resilience to regular disruptive congestion events. Specifically, in the second study, we focused on alternative transit modes such as bus, metro, or bike presence in the urban areas. We utilized diversity and equity concepts for assessing the opportunities they provide for people as alternative mobility modes. The proposed metrics not only capture the physical attributes of the multi-modal transportation systems (i.e. availability and distribution of transit modes in urban areas) but also consider the socio-economic factors (i.e. the number of people that could potentially use the transit mode). In the third study, we investigated how urban road networks' form and topology (i.e., the structure of roadway networks) could affect its resilience to recurrent congestions. We presented our findings as a case study in the Washington DC area. Results indicated a strong correlation between resilience and resourcefulness as well as topology features. The findings allow decision-makers to make more informed design and operational decisions and better incorporate the urban characteristics during the priority setting process.

The issues of performance measurement and efficiency analyses for transit industries have been gaining significance due to severe operating conditions and financial constraints in which these transit agencies provide service. In this research, we present an approach to measure the performance of Urban Transit Networks, specifically, bus routes that comprise the network. We propose a math programming model that evaluates the efficiencies of bus routes taking into consideration, the service providers, the users and the societal perspectives. This model is based on Data Envelopment Analysis (DEA) methodology and derives from Network Theory, Network Modeling in DEA, Goal Programming & Goal-DEA and 'Environmental' Variables. This approach enables the decision maker to determine the performance of its units of operations ('bus routes' in our case), optimally allocate scarce resources and achieve target levels for 'externality' variables for these bus routes and for the whole network. We further recommend modifications to the model, for adaptation to other modes of transportation as well as extend its applicability to other applications/scenarios. / Master of Science

Yes / Electronic government services (e-services) involve the delivery of information and services to stakeholders via the Internet, Internet of Things and other traditional modes. Despite their beneficial values, the overall level of usage (take-up) remains relatively low compared to traditional modes. They are also challenging to evaluate due to behavioral, economical, political, and technical aspects. The literature lacks a methodology framework to guide the government transformation application to improve both internal processes of e-services and institutional transformation to advance relationships with stakeholders. This paper proposes a cognitive analytics management (CAM) framework to implement such transformations. The ambition is to increase users’ take-up rate and satisfaction, and create sustainable shared values through provision of improved e-services. The CAM framework uses cognition to understand and frame the transformation challenge into analytics terms. Analytics insights for improvements are generated using Data Envelopment Analysis (DEA). A classification and regression tree is then applied to DEA results to identify characteristics of satisfaction to advance relationships. The importance of senior management is highlighted for setting strategic goals and providing various executive supports. The CAM application for the transforming Turkish e-services is validated on a large sample data using online survey. The results are discussed; the outcomes and impacts are reported in terms of estimated savings of more than fifteen billion dollars over a ten-year period and increased usage of improved new e-services. We conclude with future research.

No / The quest for best practices may lead to an increased risk of poor decision-making, especially when aiming to attain best practice levels reveals that efforts are beyond the organization’s present capabilities. This situation is commonly known as the “best practice trap”. Motivated by such observation, the purpose of the present paper is to develop a practical methodology to support better practice benchmarking, with an application to the banking sector. In this sense, we develop a two-stage hybrid model that employs Artificial Neural Network (ANN) via integration with Data Envelopment Analysis (DEA), which is used as a preprocessor, to investigate the ability of the DEA-ANN approach to classify the sampled branches of a Greek bank into predefined efficiency classes. ANN is integrated with a family of radial and non-radial DEA models. This combined approach effectively captures the information contained in the characteristics of the sampled branches, and subsequently demonstrates a satisfactory classification ability especially for the efficient branches. Our prediction results are presented using four performance measures (hit rates): percent success rate of classifying a bank branch’s performance exactly or within one class of its actual performance, as well as just one class above the actual class and just one class below the actual class. The proposed modeling approach integrates the DEA context with ANN and advances benchmarking practices to enhance the decision-making process for efficiency improvement.

Yes / For meeting the external requirements of the Paris Agreement and reducing energy consumption per gross domestic product, China needs to improve its energy efficiency. Although the existing studies have attempted to investigate energy efficiency from different perspectives, little effort has yet been made to consider the collaboration among different stages in the production chain to produce energy outputs. In addition, various studies have also examined the determinants of energy efficiency, however, they mainly focused on technology and economic factors, no study has yet proposed and considered the influence of geographical factors on energy efficiency. In this article, we fill in the gap and make theoretical and empirical contributions to the literature. In this study, a two-stage analysis method is used to analyse energy efficiency and the influencing factors in China between 2009 and 2021. More specifically, from the theoretical/methodological perspective, a multi-activity network data envelopment analysis model is used to measure energy efficiency of different processes in the energy production chain. From the empirical perspective, we attempt to investigate the influence of geographical factors on energy efficiency through a neural network analysis. Meanwhile, the comparisons among different provinces are made. The result shows that the overall energy efficiency is low in China, and China relies more on the traditional energy industry than the clean energy industry. The efficiency level experiences a level of volatility over the examined period. Finally, we find that raw fuel pre-process and industry have a significant and positive impact on energy efficiency in China.

Yes / Using a sample of Brazilian listed companies during 2010-2019, the study investigates the endogeneity and the directional cause-effect relationship between firm efficiency, market structure and firms’ ESG performance under a Stochastic Structural Relationship Programming (SSRP) model. Also, comprehensive market structure indicators are used. The efficiency is estimated under a two-stage network Data Envelopment Analysis (NDEA) model. Our empirical evidence is threefold. First, our evidence indicates that firms with better environmental performance are more efficient, whereas lower ESG performance and poorer corporate governance practices are associated with a higher level of efficiency. Second, our findings suggest that market structure measures (i.e., competition and market power) have heterogeneous impacts on various ESG indexes. Specifically, higher market competition is associated with better overall ESG performance and environmental performance but worse corporate governance performance, although market power can only enhance the environmental and governance performance of firms. Third, the two market structure proxies employed in this study are significantly attributed to firm efficiency. Our findings provide practical implications for various stakeholders and suggest avenues for future studies that can build on our evidence.

Data Envelopment Analysis (Charnes et al, 1978) is a technique used to evaluate the relative efficiency of any process or an organization. The efficiency evaluation is relative, which means it is compared with other processes or organizations. In real life situations different processes or units seldom operate in similar environments. Within a relative efficiency context, if units operating in different environments are compared, the units that operate in less desirable environments are at a disadvantage. In order to ensure that the comparison is fair within the DEA framework, a two-stage framework is presented in this thesis. Fuzzy clustering is used in the first stage to suitably group the units with similar environments. In a subsequent stage, a relative efficiency analysis is performed on these groups. By approaching the problem in this manner the influence of environmental variables on the efficiency analysis is removed. The concept of environmental dependency index is introduced in this thesis. The EDI reflects the extent to which the efficiency behavior of units is due to their environment of operation. The EDI also assists the decision maker to choose appropriate peers to guide the changes that the inefficient units need to make. A more rigorous series of steps to obtain the clustering solution is also presented in a separate chapter (chapter 5). / Master of Science

This dissertation brings together the areas of safety science and operations management through a mixed-methods approach to investigate the complex relationships between two, often conflicting, organizational goals - efficiency and safety, in sociotechnical systems (STSs). This research mainly focuses on production pressure (PrP) which is considered as one of the main negative outcomes of overprioritizing the efficiency aspect of STSs. This work seeks to introduce novel methodologies for assessing PrP in real time for the purpose of mitigating its risks and unwanted consequences, particularly in safety critical environments such as traffic control centers (TCCs). Essay 1 concentrates on the theoretical underpinnings of PrP by systematically reviewing the existing literature to clarify and unify the concept under the context of safety science. It identifies key factors contributing to PrP, its negative effects on safety performance in various industries, and potential mitigation strategies. By doing so, this essay contributes to the field through laying the groundwork for more effective management strategies to improve workplace safety. Essay 2 addresses a significant gap identified in Essay 1 by developing a methodology based on Data Envelopment Analysis (DEA) for the ongoing measurement and monitoring of PrP. This innovative approach introduces a quantitative mechanism that juxtaposes efficiency and safety related outcomes of hourly performance in safety critical environments. This proposed method allows for a detailed analysis of performance dynamics within STSs. The practical application of this model is demonstrated through its implementation in the infrastructure management system of INFRABEL, the Belgian National Railroad Company. Essay 3 advances the conversation by tackling the predictive limitations of the DEA model established in Essay 2. It integrates Machine Learning (ML) techniques with DEA to develop an innovative method for forecasting near-future PrP levels for proactive management of safety risks. The major contribution of Essay 3 is the novel interface between ML and DEA that can improve decision-making capabilities of managers in safety-critical STSs through real-time monitoring and predictive analytics. Together, these studies contribute to the theoretical discussions around PrP and present practical solutions to longstanding challenges in safety science and operational management. / Doctor of Philosophy / In today's increasingly complex world, the systems that run our industries, from traffic control to healthcare, face a dilemmatic balance between pushing for higher productivity and ensuring safety. This dissertation explores the trade-offs between efficiency and safety which has become more pronounced with the advancement of technology. Traditional safety approaches which used to be effective in simpler systems, struggle in modern STSs where causes and effects are not linear but tangled in a web of unpredictable interactions. Production pressure (PrP), at the core of the mentioned balance, is the drive to maximize output and efficiency, often at the expense of safety. This pressure can lead to unintended and sometimes catastrophic outcomes in the long term, especially in environments where safety is critical, such as rail traffic control centers. Despite its vital impact, there has been a noticeable gap in understanding and managing PrP. In fact, existing safety frameworks are struggling to capture the dynamic nature of PrP, consequently, its real-time measurement and control remain difficult to achieve. This work, therefore, tries to broaden our understanding of PrP and to develop methods to monitor, measure, and predict it, to equip managers and policymakers with the tools to navigate the efficiency-safety dichotomy more effectively. Through a series of essays, this dissertation reviews the current state of knowledge on PrP to identify its sources and impacts and also innovates a novel approach to quantify PrP in real-time and predict its future trends.