The Architecture Design of Electrical Vehicle Infrastructure Using Viable System Model Approach

Exponential technological-based growth in industrialization and urbanization, and the ease of mobility that modern motorization offers have significantly transformed social structures and living standards. As a result, electric vehicles (EVs) have gained widespread popularity as a mode of sustainable transport. The increasing demand for of electric vehicles (EVs) has reduced the some of the environmental issues and urban space requirements for parking and road usage. The current body of EV literature is replete with different optimization and empirical approaches pertaining to the design and analysis of the EV ecosystem; however, probing the EV ecosystem from a management perspective has not been analyzed. To address this gap, this paper develops a systems-based framework to offer rigorous design and analysis of the EV ecosystem, with a focus on charging station location problems. The study framework includes: (1) examination of the EV charging station location problem through the lens of a systems perspective; (2) a systems view of EV ecosystem structure; and (3) development of a reference model for EV charging stations by adopting the viable system model. The paper concludes with the methodological implications and utility of the reference model to offer managerial insights for practitioners and stakeholders

Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports

Development of a New Instrument to Assess the Performance of Systems Engineers

System engineering (SE) is a structured systematized methodology that deals with designing, managing, and optimizing systems performance. System engineers use the perspective of system thinking to make the successful use and retirement of engineering systems. Since the role of system engineers ranges widely from technical support to customer interaction, system design to management, there is a demand to develop a cadre of effective systems engineers. However, two critical questions are not well-defined in the extant body of SE literature: (1) What are the fundamental attributes of systems engineering that would influence the performance/effectiveness of individual systems engineer? (2) What are the corresponding leading indicators for appraising the performance of an individual systems engineer? To respond to these questions, this study proposes a new instrument to evaluate the system engineers' performance and subsequently identify their strengths and weaknesses within the complex system domain. The instrument is based on the set of performance indicators examining six fundamental system engineering attributes. The implication of this study would assist systems engineers in strengthening their system skills and reflects a state that can be improved through training, workshops, and education to prepare them to face the complex situations originating from the problem domain

A Framework for Modeling and Assessing System Resilience Using a Bayesian Network: A Case Study of an Interdependent Electrical Infrastructure System

This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience using the Bayesian network. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system

Assessing blockchain technology adoption in the Norwegian oil and gas industry using Bayesian Best Worst Method

Despite the promising features of blockchain, such as enhancing efficiency, transparency, immutability, cost savings, and traceability, the technology is still not widely adopted across industries. The oil and gas industry uses state-of-the-art engineering solutions for oil and gas exploration but substantially lags behind in using innovative digital technologies that can improve operational excellence. This study proposes a multi-criteria decision-making (MCDM) framework for assessing blockchain adoption strategies. The framework builds on critical factors for blockchain adoption and four adoption strategies — single use, localization, substitution, and transformation. Data were collected from ten experts in the Norwegian oil and gas industry using a structured web survey. The Bayesian Best Worst Method (BWM), a probabilistic MCDM method, was used for analysis. The results suggest that three sub-criteria, which are lack of expertise about technology, lack of supply chain partner collaboration, and reducing operation cost, have the most impact on the adoption process. As for blockchain adoption alternatives, the fourth phase, that is, transformation, is the most preferred in the context of the Norwegian oil and gas industry. The proposed framework lays the foundation for companies to understand the critical elements that need improvement to accelerate the blockchain technology adoption process

Systems Thinking: A Review and Bibliometric Analysis

Systems thinking (ST) is an interdisciplinary domain that offers different ways to better understand the behavior and structure of a complex system. Over the past decades, several publications can be identified in academic literature, focusing on different aspects of systems thinking. However, two critical questions are not properly addressed in the extant body of ST literature: (i) How to conduct the content analysis exclusively to derive the prominent statistics (i.e., influential journals, authors, affiliated organizations and countries) pertaining to the domain of ST? (ii) How to get better insights regarding the current and emerging trends that may evolve over time based on the existing body of ST literature? To address these gaps, the aim of this research study is to provide a comprehensive insight into the domain of systems thinking through bibliometric and network analysis. Beginning with over 6000 accumulated publications, the analysis narrowed down to 626 prominent articles with proven influence published over the past three decades. Leveraging rigorous bibliometric tools analysis, this research unveils the influential authors, leading journals and top contributing organizations and countries germane to the domain of systems thinking. In addition, citation, co-citation and page rank analysis used to rank top influential articles in the area of systems thinking. Finally, with the aid of the network analysis, key clusters in the existing literature are identified based on the research areas of systems thinking. The findings of this research will serve as a bluebook for practitioners and scholars to conduct future research within systems thinking context

Modeling and Analysis of Unmanned Aerial Vehicle System Leveraging Systems Modeling Language (SysML)

The use of unmanned aerial vehicles (UAVs) has seen a significant increase over time in several industries such as defense, healthcare, and agriculture to name a few. Their affordability has made it possible for industries to venture and invest in UAVs for both research and commercial purposes. In spite of their recent popularity; there remain a number of difficulties in the design representation of UAVs, including low image analysis, high cost, and time consumption. In addition, it is challenging to represent systems of systems that require multiple UAVs to work in cooperation, sharing resources, and complementing other assets on the ground or in the air. As a means of compensating for these difficulties; in this study; we use a model-based systems engineering (MBSE) approach, in which standardized diagrams are used to model and design different systems and subsystems of UAVs. SysML is widely used to support the design and analysis of many different kinds of systems and ensures consistency between the design of the system and its documentation through the use of an object-oriented model. In addition, SysML supports the modeling of both hardware and software, which will ease the representation of both the system’s architecture and flow of information. The following paper will follow the Magic Grid methodology to model a UAV system across the SysML four pillars and integration of SysML model with external script-based simulation tools, namely, MATLAB and OpenMDAO. These pillars are expressed within standard diagram views to describe the structural, behavior, requirements, and parametric aspect of the UAV. Finally, the paper will demonstrate how to utilize the simulation capability of the SysML model to verify a functional requirement

Assessing blockchain technology adoption in the Norwegian oil and gas industry using Bayesian Best Worst Method

Despite the promising features of blockchain, such as enhancing efficiency, transparency, immutability, cost savings, and traceability, the technology is still not widely adopted across industries. The oil and gas industry uses state-of-the-art engineering solutions for oil and gas exploration but substantially lags behind in using innovative digital technologies that can improve operational excellence. This study proposes a multi-criteria decision-making (MCDM) framework for assessing blockchain adoption strategies. The framework builds on critical factors for blockchain adoption and four adoption strategies — single use, localization, substitution, and transformation. Data were collected from ten experts in the Norwegian oil and gas industry using a structured web survey. The Bayesian Best Worst Method (BWM), a probabilistic MCDM method, was used for analysis. The results suggest that three sub-criteria, which are lack of expertise about technology, lack of supply chain partner collaboration, and reducing operation cost, have the most impact on the adoption process. As for blockchain adoption alternatives, the fourth phase, that is, transformation, is the most preferred in the context of the Norwegian oil and gas industry. The proposed framework lays the foundation for companies to understand the critical elements that need improvement to accelerate the blockchain technology adoption process

A decision support model for assessing and prioritization of industry 5.0 cybersecurity challenges

The world is adopting the Industry 5.0 paradigm to increase human centricity, sustainability, and resilience in efficient, optimized, and profitable manufacturing systems. With benefits, however, come increased risks of economic and physical loss, driving the need for continuous improvement of Industry 5.0 cybersecurity. Implementation and advancement of adequate cybersecurity have created challenges that have been identified in the literature. In this study, key Industry 5.0 cybersecurity challenges and related sub-challenges are highlighted based on a literature review. Graph Theory and Matrix Approach (GTMA) is employed to analyze the challenges and determine relative importance based on permanent values of the variable permanent matrix (VPM). The results identify the most important Industry 5.0 cybersecurity challenges and reveal Industry 5.0 firms should primarily concentrate on supply chain vulnerabilities to decrease data loss and hacking in the organization's supply chain network. This study also recommends that executives and lawmakers acquire knowledge regarding cybersecurity challenges and prepare to deal with them. Addressing these and other subsequently prioritized challenges—the top five rounded out with emergent cybersecurity trends, non-availability of cybersecurity curriculum in education, embedded technical constraints, and absence of skilled employees and training—will lead the methodical development of holistic, robust cybersecurity programs. Firms accepting of this reality may implement such programs to mitigate evolving cyber-risk towards harnessing and sustaining the benefits of novel Industry 5.0 technologies