多因素耦合作用下地下厂房施工进度风险分析及其应用

为深入分析地下厂房施工进度风险因素之间的耦合作用及耦合效应,采用WBS-RBS法识别了影响施工进度的人、物、环境、管理4类风险因素,基于系统动力学方法揭示了同质单因素和异质双-多因素耦合关系,在此基础上建立了同质单因素耦合度模型和异质双-多因素N-K模型,运用蒙特卡罗法模拟了各风险因素对进度影响的概率分布,分析耦合强度。工程实例应用表明,各类风险子系统中存在较大的同质单因素耦合风险,应加强针对性预防和控制;异质双-多因素耦合中,有管理因素参与耦合的风险较大,实际施工中应尽可能减少多因素参与耦合。     

Adaptation to climate change and conservation of biodiversity using green infrastructure

In recent years, we have experienced mega‐flood disasters in Japan due to climate change. In the last century, we have been building disaster prevention infrastructure (artificial levees and dams, referred to as “grey infrastructure”) to protect human lives and assets from floods, but these hard protective measures will not function against mega‐floods. Moreover, in a drastically depopulating society such as that in Japan, farmland abandonment prevails, and it will be more difficult to maintain grey infrastructure with a limited tax income. In this study, we propose the introduction of green infrastructure (GI) as an adaptation strategy for climate change. If we can use abandoned farmlands as GI, they may function to reduce disaster risks and provide habitats for various organisms that are adapted to wetland environments. First, we present a conceptual framework for disaster prevention using a hybrid of GI and conventional grey infrastructure. In this combination, the fundamental GI, composed of forests and wetlands in the catchment (GI‐1) and additional multilevel GIs such as flood control basins that function when floodwater exceeds the planning level (GI‐2) are introduced. We evaluated the flood attenuation function (GI‐1) of the Kushiro Wetland using a hydrological model and developed a methodology for selecting suitable locations of GI‐2, considering flood risk, biodiversity and the distribution of abandoned farmlands, which represent social and economic costs. The results indicated that the Kushiro Wetland acts as a large natural reservoir that attenuates the hydrological peak discharge during floods and suitable locations for introducing GI‐2 are concentrated in floodplain areas developing in the downstream reaches of large rivers. Finally, we discussed the network structure of GI‐1 as a hub and GI‐2 as a dispersal site for conservation of the Red‐crowned Crane, one of the symbolic species of Japan.     

Supply chain data analytics for predicting supplier disruptions: a case study in complex asset manufacturing

Although predictive machine learning for supply chain data analytics has recently been reported as a significant area of investigation due to the rising popularity of the AI paradigm in industry, there is a distinct lack of case studies that showcase its application from a practical point of view. In this paper, we discuss the application of data analytics in predicting first tier supply chain disruptions using historical data available to an Original Equipment Manufacturer (OEM). Our methodology includes three phases: First, an exploratory phase is conducted to select and engineer potential features that can act as useful predictors of disruptions. This is followed by the development of a performance metric in alignment with the specific goals of the case study to rate successful methods. Third, an experimental design is created to systematically analyse the success rate of different algorithms, algorithmic parameters, on the selected feature space. Our results indicate that adding engineered features in the data, namely agility, outperforms other experiments leading to the final algorithm that can predict late orders with 80% accuracy. An additional contribution is the novel application of machine learning in predicting supply disruptions. Through the discussion and the development of the case study we hope to shed light on the development and application of data analytics techniques in the analysis of supply chain data. We conclude by highlighting the importance of domain knowledge for successfully engineering features.     

Viability of intertwined supply networks: extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak

An intertwined supply network (ISN) is an entirety of interconnected supply chains (SC) which, in their integrity secure the provision of society and markets with goods and services. The ISNs are open systems with structural dynamics since the firms may exhibit multiple behaviours by changing the buyer-supplier roles in interconnected or even competing SCs. From the positions of resilience, the ISNs as a whole provide services to society (e.g. food service, mobility service or communication service) which are required to ensure a long-term survival. The analysis of survivability at the level of ISN requires a consideration at a large scale as resilience of individual SCs. The recent example of coronavirus COVID-19 outbreak clearly shows the necessity of this new perspective. Our study introduces a new angle in SC resilience research when a resistance to extraordinary disruptions needs to be considered at the scale of viability. We elaborate on the integrity of the ISN and viability. The contribution of our position study lies in a conceptualisation of a novel decision-making environment of ISN viability. We illustrate the viability formation through a dynamic game-theoretic modelling of a biological system that resembles the ISN. We discuss some future research areas.     

第七届世界军人运动会核与辐射风险评估与控制

武汉市涉核单位众多,存在一定的核与辐射事故风险。通过对武汉市举办第七届世界军人运动会可能存在的核与辐射风险因素进行识别及评价,确定可能发生的12类核与辐射事故类型及风险水平,进而有针对性的制定风险防控措施,做到辐射应急管理关口前移,在保证军运会期间核技术利用单位正常生产活动的基础上有效的保障了核与辐射安全。     

Ergonomics interventions to reduce musculoskeletal risk factors in a truck manufacturing plant

Ergonomic interventions may potentially reduce MSDs, but the context of industries (barriers, ever-changing situations, dialogue processes) might play a significant role in the success of interventions. This study evaluates the effectiveness of ergonomic interventions including engineering/technical and organizational interventions, and the involvement of the stakeholders in reducing musculoskeletal risk factors/symptoms. A pre-post-test experimental study in non-randomized groups was performed over three years in a sector of a truck assembly plant. The mean age of the operators in the sector for the initial and second assessment time was 42.0 (±7.6) years and 39.0 (±8.7), respectively. The mean length of work experience in the current job was 15.2 (±7.2) years and 13.9 (±7.3) for the initial and second assessment times, respectively. Five engineering ergonomic solutions and organizational interventions were implemented after a comprehensive ergonomic analysis. The organizational interventions consisted mostly of transferring and redistributing the tasks, i.e., ergonomically balancing and redesigning of the workstations. Before performing the interventions, the findings of the ergonomic study were presented at several meetings to encourage the involvement of the stakeholders (including managers, engineers, and operators) in the interventions. This study showed that a combination of ergonomic measures—engineering and organizational interventions—could reduce physical workloads. Musculoskeletal symptoms decreased after interventions although the difference was not significant.     

Sustainable disassembly line balancing model based on triple bottom line

End of life (EOL) phase of a product is receiving more attention due to increase in environmental concerns, and many studies have been conducted for value creation in EOL, focusing on concepts as remanufacturing, reuse and recycling in sustainable production manner. This study especially focuses on one of global problem, e-waste. To minimise the amount of wastes and maximise recovered materials from EOL, disassembly is one of the most important concept, associated with reuse, and balancing disassembly line in an optimal way is essential for organisations. In disassembly line balancing (DLB), not only precedence of tasks, but also risk criteria related to environment and human safety should be considered for sustainability. The aim of this study is to propose a model based on triple bottom line (TBL) dimensions, i.e. human safety, environmental safety and business criteria. To achieve sustainability in DLB, and for risk assessment in sustainable DLB, it had been decided to use a multi-criteria method, i.e. TODIM, acronym in Portuguese of ‘Tomada de Decisão Iterativa Multicritério’. The proposed model included 22 disassembly criteria categorised under TBL dimensions, which are derived from the literature. Implementation of the study was conducted for computer disassembly processes, and as a result of the study approximately 12% an improvement in cycle time was succeeded. In the long run, the integration of sustainability in disassembly operations may contribute to the competitive advantage of the company in terms of differentiation and corporate image by achieving business, environment and human targets simultaneously.     

面向电网实时运行风险的快速定级方法研究及应用

为满足电网运行实时风险评估时效性要求,解决电网运行实时风险评估与定级中效率与精度两者间协调的问题,在当前国家和主要电网企业规定的电网运行事故定级标准基础上提出了一种快速定级方法。快速定级方法充分考虑了电网运行实际特性,采取数据治理和算法重构两方面主要措施,对原判定方法进行优化处理。通过数据治理,大幅降低了对外部数据的依赖性,并取消了部分不必要的输入数据。采用算法重构,进一步将运行风险定级与实际业务流程紧密联系。以该快速定级方法为核心,构建了支撑电网运行风险分析与事故处置的实时运行风险管理系统,介绍了该系统的功能框架与系统架构。最后结合该系统在贵阳的实际应用情况,分析了快速定级方法的实际使用效益。     

净水水处理剂中有害杂质在水中残留物分析与风险评估

与欧盟和美国标准相比,我国硫酸铝和硫酸铵标准缺少对水处理剂中有害杂质镍、锑、硒、银、吡啶、氰化物、六价铬的检测;次氯酸钠缺少对水处理剂中有害杂质汞、铬、镉、铁、镍、锑、硒、银、六价铬的检测。结合某市2019年上半年13个水厂出厂水和原水的检测数据,剔除了5项始终低于检出限的有害杂质指标,以及2项出厂水浓度低于原水浓度的有害杂质指标。建议该市硫酸铝和硫酸铵的检测项目可以在原有国标基础上增加锑、硒;次氯酸钠可以增加汞、锑、硒。     

Risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles using downside risk constraints method

Today, the utilizations of hydrogen storage systems (HSS), renewable generation units (PV and wind generation) and distributed energy units are increased in the intelligent parking lots (IPL) in order to charge the electric vehicles (EVs) with clean energy sources. In this work, the uncertainties of upstream grid price, the demand of IPL, wind speed, solar irradiation and temperature are modeled via scenario approach based on stochastic programing. Furthermore, the downside risk constraints method (DRCM) is applied to consider risk related to uncertainties to get risk-involved stochastic performance of hydrogen storage based intelligent parking lots of electric vehicles. The proposed risk-based formulation is modeled using mixed-integer linear programming (MIP) which is implemented under GAMS software and solved via CPLEX solver. Two cases namely risk-averse and risk-neutral strategies are studied and compared to show the effects of DRCM implementation. The obtained results demonstrate the expected performance cost (EPC) of IPL is slowly raised while risk-in-cost (RIC) is significantly reduced due to model of risk related to uncertainties.