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Analysis of Supply Chain Sustainability Drivers in the Oil and Gas Industry under Covid-19 Pandemic

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Journal of systems Science and systems engineering 2024年第2期33卷 131-161页

作者： Sujan Piya department of industrial engineering and engineering management University of SharjahSharjahUnited Arab Emirates

The supply chain of many industries,including Oil and Gas,was significantly affected by the disruption caused by the Covid pandemic.This,in turn,had a knock-on effect on other industries around the globe.Sustaining the impact of the disruption posed a major challenge for the industry.This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic.Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry.Multi-criteria decision-making methodologies were used to analyze these drivers.The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were"Risk management capacity","Government regulation"and"Health and safety of employees".On the other hand,the driver"Community Pressure"was found to be of the least importance.Furthermore,the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index.A case example was demonstrated to rank the industries based on such calculations.This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index.Additionally,the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption.This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future.

关键词： Supply chain sustainability drivers Oil and gas supply chain Fuzzy analytical hierarchy process Fuzzy technique for order of preference by similarity to ideal solution Covid-19

Review on the state-of-the-art and challenges in the MgB_(2)component manufacturing for superconducting applications

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Superconductivity 2024年第1期9卷 50-64页

作者： Fabiano Carvalho de Castro Sene department of industrial and systems engineering University of TennesseeKnoxvilleUnited States

Since the discovery of MgB_(2)as a superconductor,several research groups worldwide have studied the superconducting mechanisms due to the dual gap nature of MgB_(2),as well as attempted to produce such a compound in wires,tapes,bulks,and thin films for a plethora of applications.While MgB_(2)carries the promise of replacing Niobium‐based superconductors in low‐field applications,less‐than‐desirable performance and in‐operation stability has slowed down such a progress.While the properties and nature of the superconductivity of MgB_(2)are fairly known,the reproduction of its properties at manufacturing scales remains an unsolved problem.Therefore,this manuscript presents a systematic review on fundamental properties,phase formation,growth kinetics,and superconducting properties of MgB_(2)‐based components such as multi‐and mono‐core wires,bulks,and thin films.Advances,challenges,and shortcomings are utilized in consolidating research questions and directions pertaining to the manufacturing of MgB_(2)superconducting devices.Lastly,we evaluate the technological readiness of MgB_(2)‐based devices for applications in fusion energy systems.

关键词： MgB_(2) Superconductor manufacturing Technology readiness Review Low‐Field applications

Generating synthetic as-built additive manufacturing surface topography using progressive growing generative adversarial networks

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Friction 2024年第6期12卷 1283-1298页

作者： Junhyeon SEO Prahalada RAO Bart RAEYMAEKERS department of Mechanical engineering Virginia TechBlacksburgVA 24061USA Grado department of industrial and systems engineering Virginia TechBlacksburgVA 24061USA

Numerically generating synthetic surface topography that closely resembles the features and characteristics of experimental surface topography measurements reduces the need to perform these intricate and costly measurements.However,existing algorithms to numerically generated surface topography are not well-suited to create the specific characteristics and geometric features of as-built surfaces that result from laser powder bed fusion(LPBF),such as partially melted metal particles,porosity,laser scan lines,and balling.Thus,we present a method to generate synthetic as-built LPBF surface topography maps using a progressively growing generative adversarial network.We qualitatively and quantitatively demonstrate good agreement between synthetic and experimental as-built LPBF surface topography maps using areal and deterministic surface topography parameters,radially averaged power spectral density,and material ratio curves.The ability to accurately generate synthetic as-built LPBF surface topography maps reduces the experimental burden of performing a large number of surface topography measurements.Furthermore,it facilitates combining experimental measurements with synthetic surface topography maps to create large data-sets that facilitate,e.***.relating as-built surface topography to LPBF process parameters,or implementing digital surface twins to monitor complex end-use LPBF parts,amongst other applications.

关键词： additive manufacturing surface topography synthetic surface topography generative adversarial networks

Deep Reinforcement Learning or Lyapunov Analysis?A Preliminary Comparative Study on Event-Triggered Optimal Control

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1702-1704页

作者： Jingwei Lu Lefei Li Qinglai Wei Fei-Yue Wang IEEE the department of industrial engineering Tsinghua UniversityBeijing 100084China The State Key Laboratory for management and Control of Complex systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Dear Editor,This letter develops a novel method to implement event-triggered optimal control(ETOC) for discrete-time nonlinear systems using parallel control and deep reinforcement learning(DRL), referred to as Deep-ETOC. The developed Deep-ETOC method introduces the communication cost into the performance index through parallel control, so that the developed method enables control systems to learn ETOC policies directly without triggering conditions.

关键词： Deep letter enable

Energy-Saving Distributed Flexible Job Shop Scheduling Optimization with Dual Resource Constraints Based on Integrated Q-Learning Multi-Objective Grey Wolf Optimizer

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Computer Modeling in engineering & Sciences 2024年第8期140卷 1459-1483页

作者： Hongliang Zhang Yi Chen Yuteng Zhang Gongjie Xu School of management Science and engineering Anhui University of TechnologyMa’anshan243032China Laboratory of Multidisciplinary management and Control of Complex systems of Anhui Higher Education Institutes Anhui University of TechnologyMa’anshan243032China department of industrial engineering School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi’an710072China

The distributed flexible job shop scheduling problem(DFJSP)has attracted great attention with the growth of the global manufacturing industry.General DFJSP research only considers machine constraints and ignores worker constraints.As one critical factor of production,effective utilization of worker resources can increase productivity.Meanwhile,energy consumption is a growing concern due to the increasingly serious environmental issues.Therefore,the distributed flexible job shop scheduling problem with dual resource constraints(DFJSP-DRC)for minimizing makespan and total energy consumption is studied in this paper.To solve the problem,we present a multi-objective mathematical model for DFJSP-DRC and propose a Q-learning-based multi-objective grey wolf optimizer(Q-MOGWO).In Q-MOGWO,high-quality initial solutions are generated by a hybrid initialization strategy,and an improved active decoding strategy is designed to obtain the scheduling schemes.To further enhance the local search capability and expand the solution space,two wolf predation strategies and three critical factory neighborhood structures based on Q-learning are proposed.These strategies and structures enable Q-MOGWO to explore the solution space more efficiently and thus find better Pareto solutions.The effectiveness of Q-MOGWO in addressing DFJSP-DRC is verified through comparison with four algorithms using 45 instances.The results reveal that Q-MOGWO outperforms comparison algorithms in terms of solution quality.

关键词： Distributed flexible job shop scheduling problem dual resource constraints energy-saving scheduling multi-objective grey wolf optimizer Q-learning

Evaluating innovation ecosystem resiliency using agent-based modeling and systems dynamics

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Journal of Safety Science and Resilience 2024年第2期5卷 204-221页

作者： Soheila Abdi Mehdi Yazdani Esmaeil Najafi department of industrial engineering Science and Research BranchIslamic Azad UniversityTehranIran department of industrial engineering Qazvin BranchIslamic Azad UniversityQazvinIran

Evaluating the resilience of the innovation ecosystem to maintain its performance,in the sense of resistance to disruption and recovery after it,has recently received more attention.Several studies have been conducted to model different ecosystems and evaluate their resilience.However,modeling the innovation ecosystem from a holistic perspective and performing a quantitative assessment of its resilience have received less attention.This paper models the innovation ecosystem holistically and evaluates its resilience index using a quantitative approach through five main steps.In the first step,a case study related to the innovation ecosystem of Iran's Ministry of Energy,called the Power Innovation Ecosystem,is modeled by combining system dynamics and agent-based modeling.Upon validating the model in the second step,the disruption of the loss of experts is investigated in the third step,and all possible actions to recover each actor are analyzed.In the fourth step,the performance of the ecosystem is simulated before and after the disruption using the data gathered in the previous steps.Finally,resilience is calculated in two different ways in the fifth step.Several improvement solutions are also suggested when considering that the resilience index of the innovation ecosystem is at a medium level.This research may assist policymakers in observing the resilience level of the innovation ecosystem based on the proposed model.By applying strategic changes to this model,they can determine the effects of their policies and make the most appropriate decisions to increase the resilience of the innovation ecosystem.

关键词： Resilience index innovation ecosystem System dynamics Agent-based modeling

Research on Driver’s Fatigue Detection Based on Information Fusion

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Computers, Materials & Continua 2024年第4期79卷 1039-1061页

作者： Meiyan Zhang Boqi Zhao Jipu Li Qisong Wang Dan Liu Jinwei Sun Jingxiao Liao Harbin Institute of Technology School of Instrument Science and EngineeringHarbin150036China department of industrial and systems engineering The Hong Kong Polytechnic UniversityHong Kong100872China

Driving fatigue is a physiological phenomenon that often occurs during driving.After the driver enters a fatigued state,the attentionis lax,the response is slow,and the ability todeal with emergencies is significantly reduced,which can easily cause traffic accidents.Therefore,studying driver fatigue detectionmethods is significant in ensuring safe driving.However,the fatigue state of actual drivers is easily interfered with by the external environment(glasses and light),which leads to many problems,such as weak reliability of fatigue driving detection.Moreover,fatigue is a slow process,first manifested in physiological signals and then reflected in human face images.To improve the accuracy and stability of fatigue detection,this paper proposed a driver fatigue detection method based on image information and physiological information,designed a fatigue driving detection device,built a simulation driving experiment platform,and collected facial as well as physiological information of drivers during driving.Finally,the effectiveness of the fatigue detection method was evaluated.Eye movement feature parameters and physiological signal features of drivers’fatigue levels were extracted.The driver fatigue detection model was trained to classify fatigue and non-fatigue states based on the extracted features.Accuracy rates of the image,electroencephalogram(EEG),and blood oxygen signals were 86%,82%,and 71%,separately.Information fusion theory was presented to facilitate the fatigue detection effect;the fatigue features were fused using multiple kernel learning and typical correlation analysis methods to increase the detection accuracy to 94%.It can be seen that the fatigue driving detectionmethod based onmulti-source feature fusion effectively detected driver fatigue state,and the accuracy rate was higher than that of a single information source.In summary,fatigue drivingmonitoring has broad development prospects and can be used in traffic accident prevention and wearable driver fatigue recognition.

关键词： Driving fatigue information fusion EEG blood oxygen

Improve the industrial digital transformation through industrial Internet platforms

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Frontiers of engineering management 2024年第1期11卷 167-174页

作者： Ran LIU Xiaolei XIE department of industrial engineering&management Shanghai Jiao Tong UniversityShanghai 200240China department of industrial engineering Tsinghua UniversityBeijing 100084China

1 Introduction 1.1 Definition and stakeholders of the industrial Internet The industrial Internet is a novel network technology system that applies the Internet of Things(IoT)to the manufacturing and industrial sectors.It facilitates the connection of various sensors,devices,systems,factories,and enterprises,enabling the sharing and exchange of data to improve production efficiency,reduce costs,enhance product quality,and enable smart manufacturing.The industrial Internet serves as a crucial cornerstone and essential support for the Fourth industrial Revolution,offering specific approaches for industrial digital transformation.Through the comprehensive interconnection of people,machines,and things,the industrial Internet analyzes various data from intelligent feedback,fostering the development of a new manufacturing and service system.

关键词： industrial digital transformation industrial Internet platform manufacturing big data

Resilence Control of DC Microgrid Integrated with Multi-Nanogrids Considering Connected-Islanded States

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CSEE Journal of Power and Energy systems 2024年第2期10卷 786-796页

作者： Hossien Faraji Reza Hemmati Pierluigi Siano department of Electrical engineering Amirkabir University of TechnologyTehranIran department of Electrical engineering Kermanshah University of TechnologyKermanshahIran department of management&Innovation systems University of Salernoand also with the Department of Electrical and Electronic Engineering ScienceUniversity of JohannesburgJohannesburg 2006South Africa

In this paper, a DC microgrid (DCMG) integrated with a set of nano-grids (NG) is studied. DCMG exchanges predetermined active and reactive power with the upstream network. DCMG and NGs are coordinately controlled and managed in such a way the exchanged P-Q power with external grid are kept on scheduled level following all events and operating conditions. The proposed control system, in addition to the ability of mutual support between DCMG and NGs, makes NGs support each other in critical situations. On the other hand, in all operating conditions, DCMG not only feeds three-phase loads with time-varying active and reactive power on the grid side but also injects constant active power into the grid. During events, NGs support each other, NGs support DCMG, and DCMG supports NGs. Such control strategies are realized by the proposed control method to increase resilience of the system. For these purposes, all resources and loads in DCMG and NGs are equipped with individual controllers. Then, a central control unit analyzes, monitors, and regularizes performance of individual controllers in DCMG and NGs. Nonlinear simulations show the proposed model can effectively control DCMG and NGs under normal and critical conditions.

关键词： Control system DC microgrid nanogrid resilience