The Impact of Online Logistics Service Quality Review Information on Consumers\u27 Purchase Intention

This paper constructs the index dimension of online logistics service quality review information from the two aspects of e-commerce logistics service quality factors and online review information characteristic factors, builds a structural equation model of online logistics service quality reviews information on consumers\u27 purchase intention. Empirical method is used to verify and analyze the model and hypothesis. The results show that six variables of e-commerce logistics service quality, including timeliness, reliability, empathy and online review information, such as value, quality and amount, acting on consumers\u27 purchase intention, timeliness indirectly affects consumers\u27 purchase intention through the value and amount of online review information, while empathy indirectly affects consumers\u27 purchase intention through the value of online review information. On this basis, the analysis results are discussed, and relevant suggestions are put forward for logistics enterprises and online shop sellers to create a good environment for consumers online shopping

A Review of the Criteria and Methods of Reverse Logistics Supplier Selection

This article presents a literature review on reverse logistics (RL) supplier selection in terms of criteria and methods. A systematic view of past work published between 2008 and 2020 on Web of Science (WOS) databases is provided by reviewing, categorizing, and analyzing relevant papers. Based on the analyses of 41 articles, we propose a three-stage typology of decision-making frameworks to understanding RL supplier selection, including (a) establishment of the selection criteria; (b) calculation of the relative weights and ranking of the selection criteria; (c) ranking of alternatives (suppliers). The main discoveries of this review are as follows. (1) Attention to the field of RL supplier selection is increasing, as evidenced by the increasing number of papers in the field. With the adaption of circular economy legislation and the need resource and business resilience, it is expected that RL and RL supplier selection will be a hot topic in the near future. (2) A large number of papers take “sustainability” as the theoretical approach to carry out research and use it as the basis for determining the criteria. (3) Multi-criteria decision making (MCDM) methods have been widely used in RL supplier selection and have been constantly innovated. (4) Artificial intelligence methods are also gradually being applied. Finally, gaps in the literature are identified to provide directions for future research. (5) Value-added service is underrepresented in the current study and needs further attention

An Integrated Multicriteria Decision-Making Approach for Collection Modes Selection in Remanufacturing Reverse Logistics

Reverse logistics (RL) is closely related to remanufacturing and could have a profound impact on the remanufacturing industry. Different from sustainable development which is focused on economy, environment and society, circular economy (CE) puts forward more requirements on the circularity and resource efficiency of manufacturing industry. In order to select the best reverse logistics provider for remanufacturing, a multicriteria decision-making (MCDM) method considering the circular economy is proposed. In this article, a circularity dimension is included in the evaluation criteria. Then, analytic hierarchy process (AHP) is used to calculate the global weights of each criterion, which are used as the parameters in selecting RL providers. Finally, technique for order of preference by similarity to ideal solution (TOPSIS) is applied to rank reverse logistics providers with three different modes. A medium-sized engine manufacturer in China is taken as a case study to validate the applicability and effectiveness of the proposed framework

A Review on Remanufacturing Reverse Logistics Network Design and Model Optimization

Remanufacturing has gained great recognition in recent years due to its economic and environmental benefits and effectiveness in the value retention of waste products. Many studies on reverse logistics have considered remanufacturing as a key node for network optimization, but few literature reviews have explicitly mentioned remanufacturing as a main feature in their analysis. The aim of this review is to bridge this gap. In total, 125 papers on remanufacturing reverse logistics network design have been reviewed and conclusions have been drawn from four aspects: (1) in terms of network structure, the functional nodes of new hybrid facilities and the network structure combined with the remanufacturing technologies of products are the key points in the research. (2) In the mathematical model, the multi-objective function considered from different aspects, the uncertainty of recovery time and recovery channel in addition to quantity and quality, and the selection of appropriate algorithms are worth studying. (3) While considering product types, the research of a reverse logistics network of some products is urgently needed but inadequate, such as medical and furniture products. (4) As for cutting-edge technologies, the application of new technologies, such as intelligent remanufacturing technology and big data, will have a huge impact on the remanufacturing of a reverse logistics network and needs to be considered in our research

An energy data-driven approach for operating status recognition of machine tools based on deep learning

Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent requirement to improve the fault diagnosis ability of machine tools. Through the identification of the operating state (OS) of the machine tools, defining the time point of machine tool failure and the working energy-consuming unit can be assessed. In this way, the fault diagnosis time of the machine tool is shortened and the fault diagnosis ability is improved. Aiming at the problems of low recognition accuracy, slow convergence speed and weak generalization ability of traditional OS recognition methods, a deep learning method based on data-driven machine tool OS recognition is proposed. Various power data (such as signals or images) of CNC machine tools can be used to recognize the OS of the machine tool, followed by an intuitive judgement regarding whether the energy-consuming units included in the OS are faulty. First, the power data are collected, and the data are preprocessed by noise reduction and cropping using the data preprocessing method of wavelet transform (WT). Then, an AlexNet Convolutional Neural Network (ACNN) is built to identify the OS of the machine tool. In addition, a parameter adaptive adjustment mechanism of the ACNN is studied to improve identification performance. Finally, a case study is presented to verify the effectiveness of the proposed approach. To illustrate the superiority of this method, the approach was compared with traditional classification methods, and the results reveal the superiority in the recognition accuracy and computing speed of this AI technology. Moreover, the technique uses power data as a dataset, and also demonstrates good progress in portability and anti-interference

A service-oriented energy assessment system based on BPMN and machine learning

Increasing energy cost and environmental problems push forward research on energy saving and emission reduction strategy in the manufacturing industry. Energy assessment of machining, as the basis for energy saving and emission reduction, plays an irreplaceable role in engineering service and maintenance for manufacturing enterprises. Due to the complex energy nature and relationships between machine tools, machining parts, and machining processes, there is still a lack of practical energy evaluation methods and tools for manufacturing enterprises. To fill this gap, a serviced-oriented energy assessment system is designed and developed to assist managers in clarifying the energy consumption of machining in this paper. Firstly, the operational requirements of the serviced-oriented energy assessment system are analyzed from the perspective of enterprises. Then, based on the establishment of system architecture, three key technologies, namely data integration, process integration, and energy evaluation, are studied in this paper. In this section, the energy characteristics of machine tools and the energy relationships are studied through the working states of machine tools, machining features of parts and process activities of processes, and the relational database, BPMN 2.0 specification, and machine learning approach are employed to implement the above function respectively. Finally, a case study of machine tool center stand base machining in a manufacturing enterprise was applied to verify the effectiveness and practicality of the proposed approach and system

A data-driven approach design for carbon emission prediction of machining

The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple sources and impact factors of carbon emissions in machining are studied, and the relationship between these factors is also studied to describe the carbon emissions. Then, a data-driven approach is designed to predict the carbon emission of machining, which consists of data collection and preprocessing, feature extraction, prediction model establishment and model validation. The ridge regression, BP neural network based on Genetic Algorithm (GA-BP), root means square error (RMSE) and mean relative percentage error (MPAE) are respectively employed to fulfill the above tasks in the design approach. Finally, an experimental study of a real turning machining is proposed to verify the feasibility and merits of the designed approach

Review and Classification of Bio-inspired Algorithms and Their Applications

Scientists have long looked to nature and biology in order to understand and model solutions for complex real-world problems. The study of bionics bridges the functions, biological structures and functions and organizational principles found in nature with our modern technologies, numerous mathematical and metaheuristic algorithms have been developed along with the knowledge transferring process from the lifeforms to the human technologies. Output of bionics study includes not only physical products, but also various optimization computation methods that can be applied in different areas. Related algorithms can broadly be divided into four groups: evolutionary based bio-inspired algorithms, swarm intelligence-based bio-inspired algorithms, ecology-based bio-inspired algorithms and multi-objective bio-inspired algorithms. Bio-inspired algorithms such as neural network, ant colony algorithms, particle swarm optimization and others have been applied in almost every area of science, engineering and business management with a dramatic increase of number of relevant publications. This paper provides a systematic, pragmatic and comprehensive review of the latest developments in evolutionary based bio-inspired algorithms, swarm intelligence based bio-inspired algorithms, ecology based bio-inspired algorithms and multi-objective bio-inspired algorithms

Neuroprotective Effect of Xueshuantong for Injection (Lyophilized) in Transient and Permanent Rat Cerebral Ischemia Model

Xueshuantong for Injection (Lyophilized) (XST), a Chinese Materia Medica standardized product extracted from Panax notoginseng (Burk.), is used extensively for the treatment of cerebrovascular diseases such as acutely cerebral infarction clinically in China. In the present study, we evaluated the acute and extended protective effects of XST in different rat cerebral ischemic model and explored its effect on peroxiredoxin (Prx) 6-toll-like receptor (TLR) 4 signaling pathway. We found that XST treatment for 3 days could significantly inhibit transient middle cerebral artery occlusion (MCAO) induced infarct volume and swelling percent and regulate the mRNA expression of interleukin-1β (IL-1β), IL-17, IL-23p19, tumor necrosis factor-α (TNFα), and inducible nitric oxide synthase (iNOS) in brain. Further study demonstrated that treatment with XST suppressed the protein expression of peroxiredoxin (Prx) 6-toll-like receptor (TLR) 4 and phosphorylation level of p38 and upregulated the phosphorylation level of STAT3. In permanent MCAO rats, XST could reduce the infarct volume and swelling percent. Moreover, our results revealed that XST treatment could increase the rats’ weight and improve a batch of functional outcomes. In conclusion, the present data suggested that XST could protect against ischemia injury in transient and permanent MCAO rats, which might be related to Prx6-TLR4 pathway