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1 – 10 of over 33000Yan Li, Ming K. Lim, Weiqing Xiong, Xingjun Huang, Yuhe Shi and Songyi Wang
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental…
Abstract
Purpose
Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.
Design/methodology/approach
This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.
Findings
The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.
Research limitations/implications
The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.
Originality/value
In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.
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Shahe Liang, Zhiqiang Zhang and Aiqun Li
A new type of variable damping viscous damper is developed to meet the settings of different damping parameter values at different working stages. Its main principle and design…
Abstract
Purpose
A new type of variable damping viscous damper is developed to meet the settings of different damping parameter values at different working stages. Its main principle and design structure are introduced, and the two-stage and multi-stage controllable damping methods are proposed.
Design/methodology/approach
The theoretical calculation formulas of the damping force of power-law fluid variable damping viscous damper at elongated holes are derived, aiming to provide a theoretical basis for the development and application of variable damping viscous dampers. For the newly developed variable damping viscous damper, the dynamic equations for the seismic reduction system with variable damping viscous dampers under a multi-degree-of-freedom system are established. A feasible calculation and analysis method is proposed to derive the solution process of time history analysis. At the same time, a program is also developed using Matlab. The dynamic full-scale test of a two-stage variable damping viscous damper was conducted, demonstrating that the hysteresis curve is complete and the working condition is stable.
Findings
Through the calculation and analysis of examples, the results show that the seismic reduction effect of high and flexible buildings using the seismic reduction system with variable damping viscous dampers is significant. The program developed is used to analyze the seismic response of a broadcasting tower using a variable damping TMD system under large earthquakes. The results indicate that the installation of variable damping viscous dampers can effectively control the maximum inter-story displacement response of TMD water tanks and can effectively consume seismic energy.
Originality/value
This method can provide a guarantee for the safe and effective operation of TMD in wind and vibration control.
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Dinda Thalia Andariesta and Meditya Wasesa
This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.
Abstract
Purpose
This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.
Design/methodology/approach
To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).
Findings
Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.
Originality/value
First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.
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Luciano Fratocchi, Alessandro Ancarani, Paolo Barbieri, Carmela Di Mauro, Guido Nassimbeni, Marco Sartor, Matteo Vignoli and Andrea Zanoni
The first aim of the chapter is to offer a characterization of back-reshoring as a possible step of the firm’s nonlinear internationalization process. The second aim is to review…
Abstract
Purpose
The first aim of the chapter is to offer a characterization of back-reshoring as a possible step of the firm’s nonlinear internationalization process. The second aim is to review the empirical literature on back-reshoring and to complement it with the findings of an extensive data collection.
Methodology/approach
In this chapter we adopted an explorative approach building on both theoretical and empirical literature from the fields of international business and international operations Management. We also collected secondary data on back-reshoring decisions in order to define the magnitude of the investigated phenomenon and to offer a primary characterization.
Findings
Our findings confirm that, though it cannot be considered a generalized trend, back-reshoring is a very topical issue for international business scholars. It represents an autonomous phenomenon consistent with the idea of nonlinear internationalization process.
Research limitations/implications
The chapter is based on cross-sectional data. Longitudinal research is required in order to address the proposed research questions and help understanding “how much” and what kind of manufacturing will be housed in western countries in the near future.
Originality/value
This is the first attempt to conceptualize back-reshoring as a possible step of the firms’ internationalization process. It is also the first chapter that summarizes and discusses the literature and empirical evidence on back-reshoring emerging from a wide range of countries.
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The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.
Abstract
Purpose
The purpose of this paper is to propose a new fault feature extraction scheme for the rolling element bearing.
Design/methodology/approach
The generalized Stockwell transform (GST) and the singular value ratio spectrum (SVRS) methods are combined. A time-frequency distribution measurement criterion named the energy concentration measurement (ECM) is initially used to determine the parameter of the optimal GST method. Then, the optimal GST is applied to conduct a time-frequency transformation for a raw signal. Subsequently, the two-dimensional time-frequency matrix is obtained. Finally, the improved singular value decomposition (SVD) analysis is used to conduct a noise reduction of the time-frequency matrix. The SVRS is proposed to select the effective singular values. Furthermore, the time-domain feature of the impact signal is obtained by taking the inverse GST transform.
Findings
The simulated and experimental signals are used to verify the superiority of the proposed method over conventional methods. The obtained results show that the proposed method can effectively extract fault features of the rolling element bearing.
Research limitations/implications
This paper mainly discusses the application of GST and SVRS methods to analyze the weak fault feature extraction problem. The next research direction is to explore the application of the Hilbert Huang transform (HHT) and variational modal decomposition (VMD) in the impact feature extraction of rolling bearing.
Originality/value
In the present study, a new SVRS method is proposed to select the number of effective singular values. This paper proposed an effective way to obtain the fault feature in monitoring of rotating machinery.
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Zhenhua Luo, Juntao Guo, Jianqiang Han and Yuhong Wang
Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in…
Abstract
Purpose
Prefabricated technology is gradually being applied to the construction of subway stations due to its characteristic of mechanization. However, the prefabricated subway station in China is in the initial stage of development, which is prone to construction safety issues. This study aims to evaluate the construction safety risks of prefabricated subway stations in China and formulate corresponding countermeasures to ensure construction safety.
Design/methodology/approach
A construction safety risk evaluation index system for the prefabricated subway station was established through literature research and the Delphi method. Furthermore, based on the structure entropy weight method, matter-element theory and evidence theory, a hybrid evaluation model is developed to evaluate the construction safety risks of prefabricated subway stations. The basic probability assignment (BPA) function is obtained using the matter-element theory, the index weight is calculated using the structure entropy weight method to modify the BPA function and the risk evaluation level is determined using the evidence theory. Finally, the reliability and applicability of the evaluation model are verified with a case study of a prefabricated subway station project in China.
Findings
The results indicate that the level of construction safety risks in the prefabricated subway station project is relatively low. Man risk, machine risk and method risk are the key factors affecting the overall risk of the project. The evaluation results of the first-level indexes are discussed, and targeted countermeasures are proposed. Therefore, management personnel can deeply understand the construction safety risks of prefabricated subway stations.
Originality/value
This research fills the research gap in the field of construction safety risk assessment of prefabricated subway stations. The methods for construction safety risk assessment are summarized to establish a reliable hybrid evaluation model, laying the foundation for future research. Moreover, the construction safety risk evaluation index system for prefabricated subway stations is proposed, which can be adopted to guide construction safety management.
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Zerun Fang, Wenlin Gui, Zhaozhou Han and Lan Lan
This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic…
Abstract
Purpose
This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.
Design/methodology/approach
A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.
Findings
On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.
Originality/value
This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.
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Carlo Bellavite Pellegrini, Laura Pellegrini and Emiliano Sironi
Systemic risk has been one of the most interesting issues in banking and financial literature during the last years, particularly in evaluating its effects on the stability of the…
Abstract
Systemic risk has been one of the most interesting issues in banking and financial literature during the last years, particularly in evaluating its effects on the stability of the whole financial system during crises. Differently from other studies which analyze systemic risk focusing on European countries, we explore the determinant of systemic risk in other regional or continental banking systems, as Latin America. Using the CoVaR approach proposed by Adrian and Brunnermeier (2016), we study the impact of corporate variables on systemic risk on a sample of 30 Latin American banks belonging to seven countries, continuously listed from 2002Q1 to 2015Q4. We investigate the contribution of the corporate variables over different economic periods: the Subprime crisis (2007Q3–2008Q3), the European Great Financial Depression (2008Q4–2010Q2), and the Sovereign debt crisis (2010Q3–2012Q3).
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Xu Li, Jun Li, Xiaoyi Zhang, Jianfeng Gao and Chao Zhang
Viscous dampers are commonly used in large span cable-stayed bridges to mitigate seismic effects and have achieved great success.
Abstract
Purpose
Viscous dampers are commonly used in large span cable-stayed bridges to mitigate seismic effects and have achieved great success.
Design/methodology/approach
However, the nonlinear analysis on damper parameters is usually computational intensive and nonobjective. To address these issues, this paper proposes a simplified method to determine the viscous damper parameters for double-tower cable-stayed bridges. An empirical formula of the equivalent damping ratio of viscous dampers is established through decoupling nonclassical damping structures and linearization of nonlinear viscous dampers. Shaking table tests are conducted to verify the feasibility of the proposed method. Moreover, this simplified method has been proved in long-span cable-stayed bridges.
Findings
The feasibility of this method is verified by the simplified model shaking table test. This simplified method for determining the parameters of viscous dampers is verified in cable-stayed bridges with different spans.
Originality/value
This simplified method has been validated in cable-stayed bridges with various spans.
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Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing…
Abstract
Purpose
Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.
Design/methodology/approach
The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm.
Findings
The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.
Research limitations/implications
Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.
Originality/value
The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.
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