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The purpose of this study is to suppress the temperature rise of high voltage wall bushing metal plate.

First, the authors built a model of a traditional metal plate and got the magnetic field intensity distribution by FEA tools. Optimized according to the magnetic field intensity distribution, the authors slot the traditional metal plate and embed permanent magnets in the slot. Finally, the authors got the temperature distribution diagrams of the above three cases at different current levels by FEA tools.

Slotted metal plate is beneficial to suppress magnetic induction intensity, but the improvement of the magnetic induction intensity uniformity is not obvious. The method of embedding a permanent magnet in a slotted metal plate can optimize the magnitude and uniformity of the magnetic induction intensity in the metal plate. The larger the current passing through the metal plate, the better the temperature suppression effect of the slotted metal plate and the slotted metal plate embedded in the permanent magnet.

The effect of structural factors, slotting plate and setting permanent magnets on slots on the temperature of supporting plate is studied. The paper proposes two methods, slotting metal panels and embedding permanent magnet metal panels, to solve the problems of eddy current loss and high calorific value of the panel, which is of great significance to the safety of the grid equipment.

Driven by motivation of quality enhancement and brand reputation promotion, automotive industries try to improve product quality and customer satisfaction by performing quality pilot programs continuously. The purpose of this paper is to develop a dynamic model to select the improvement quality pilot program from competitive candidates based on dynamic customers’ feedback.

An extended dynamic multi-criteria decision-making method is developed by embedding dynamic triangular fuzzy weighting average operators into fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, and the novel evaluation indicator “ζ” is introduced to reflect prioritization performance.

The two evaluation indicators (Q and “ζ” ) assist quality managers to identify the best program with respect to multiple conflicting criteria and the best choice based on these two indexes shows high conformity. Besides, ranking sequences obtained by “ζ” can avoid the dilemma that there are several candidates with top priority calculated by comprehensive group utility value Q.

The dynamic MCDM method has been applied into the quality improvement procedure in Chinese domestic auto factories and contributes to highly efficient promotion.

Few dynamic models on pilot program selection for quality improvement based on dynamic customers’ feedback, this study deals with the dynamic promotion by an extended fuzzy VIKOR method and presents a case application.

In the context of sharing economy and online shopping, establishing a stable urban joint distribution alliance (JDA) is extremely necessary for the entire logistics service market. The purpose of this paper is to rationally allocate the profits and determine the most stable allocation scheme for the urban JDA as well as provide a direction for cooperation between express enterprises and lead managers to pay more attention to the comprehensive performance.

Cooperative game-based methodologies including the proportion method, the core theory, nucleolus and Shapley value have been employed. Four criteria consisting of enterprise operation, customer satisfaction, environmental sustainability and information technology have been incorporated into Shapley value for improvement.

This paper reveals that express enterprises in logistics service market can achieve more benefit from JDA than those who operate separately. Among proposed profit allocation schemes, improved Shapley value scheme shows more rationality by considering partners’ asymmetric contribution. Besides, a stable alliance can be always ensured with partners’ lower propensity to disrupt and relatively balanced negotiation power under improved Shapley value scheme.

This paper makes a few attempts to contribute to the literature on the improvement of Shapley value and applies the concept of “propensity to disrupt” into the field of logistics. Besides, this paper provides various profit allocation schemes and incorporates influencing factors into Shapley value for an improvement thus helping policy-makers make better-informed decisions on urban distribution. Additionally, a case study based on urban express enterprises in Southwest China has been conducted to verify the proposed profit allocation schemes.

This paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)?

This paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers.

The computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.

The results provide managerial insights for express enterprises from the perspective of operation research to make decisions.

This paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.

The sudden outbreak of COVID-19 has become a major public health emergency of global concern. Studying the Internet public opinion dissemination mechanism of public health emergencies is of great significance for creating a legalized network environment, and it is also helpful for managers to make scientific decisions when encountering Internet public opinion crisis.

Based on the analysis of the process of spreading the Internet public opinion in major epidemics, a dynamic model of the Internet public opinion spread system was constructed to study the interactive relationship among the public opinion events, network media, netizens and government and the spread of epidemic public opinion. The Shuanghuanglian event in COVID-19 in China was taken as a typical example to make simulation analysis.

Research results show three points: (1) the government credibility plays a decisive role in the spread of Internet public opinion; (2) it is the best time to intervene when Internet public opinion occurred at first time; (3) the management and control of social media are the key to public opinion governance. Besides, specific countermeasures are proposed to assist control of Internet public opinion dissemination.

The epidemic Internet public opinion risk evolution system is a complex nonlinear social system. The system dynamics model is used to carry out research to facilitate the analysis of the Internet public opinion propagation mechanism and explore the interrelationship of various factors.

The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.

A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.

The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.

The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.

Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective.

Technological innovation is one of the remarkable characteristics of electric vehicles (EVs). This study aims to analyze how consumers' technological knowledge affects their intention to adopt EVs.

Original data were collected via a survey of 443 participants in China. An extended technology acceptance model was constructed to identify the factors influencing consumers' intention to adopt EVs and related technological knowledge pathways.

The results show that consumer technological knowledge is positively and significantly related to EVs' perceived usefulness, perceived ease of use, perceived fun to use and consumers' intention to adopt EVs. In addition, no direct and significant relationship is found between perceived fun to use and willingness to adopt EVs, from the technical knowledge dimension.

Imparting consumers with EV technological knowledge and usefulness may be an effective way to enhance their awareness and willingness to use EVs. Moreover, the role of females in the decision to adopt EVs should not be ignored, especially in decisions to purchase a family car.

Prior studies lack a technological knowledge-based view, and few studies have discussed how to explore the effects of consumer technological knowledge about EVs on their adoption intention. This study fills the research gap.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

This paper aims to propose a solution for dissolving the “privacy paradox” in social networks, and explore the feasibility of adopting a synergistic mechanism of “deep-learning algorithms” and “differential privacy algorithms” to dissolve this issue.

To validate our viewpoint, this study constructs a game model with two algorithms as the core strategies.

The “deep-learning algorithms” offer a “profit guarantee” to both network users and operators. On the other hand, the “differential privacy algorithms” provide a “security guarantee” to both network users and operators. By combining these two approaches, the synergistic mechanism achieves a balance between “privacy security” and “data value”.

The findings of this paper suggest that algorithm practitioners should accelerate the innovation of algorithmic mechanisms, network operators should take responsibility for users’ privacy protection, and users should develop a correct understanding of privacy. This will provide a feasible approach to achieve the balance between “privacy security” and “data value”.

These findings offer some insights into users’ privacy protection and personal data sharing.

With the rise of customer engagement in online products and services innovation, enterprises are seeking effective referral reward program (RRP) to encourage customers’ follow-up electronic-referral (e-referral) behaviors. Therefore, how to stimulate more customers to participate in the RRP is very important to enterprises. However, little empirical work has systemically investigated the impact of RRP on customers’ follow-up e-referral, as well as the moderating effects of customers’ characteristics. To fill those research gaps, the purpose of this paper is to explore the effects of RRP (particularly, reward amount and reward type) on customers’ follow-up e-referral, and the role of creative self-efficacy.

Based on the self-perception theory and the context of online customer innovation, this paper establishes a theoretical model and uses an experiment with 160 participants to test the hypotheses on the role of reward (amount and type) and the moderating effect of creative self-efficacy.

The results of the experiment suggest that both reward amount and reward type in RRP positively impact customers’ follow-up e-referral. Furthermore, customers’ creative self-efficacy moderates the relationship between rewards and customers’ follow-up e-referral. Customers with low creative self-efficacy, reward amount significantly stimulate their follow-up e-referral, but such effect is insignificant when customers’ creative self-efficacy is high. In terms of reward type, gift reward has more positive effect on customers’ follow-up e-referral when they have high (rather than low) creative self-efficacy, but cash reward has more positive effect on those with low (rather than high) creative self-efficacy.

First, based on the self-perception theory, the study clarifies the inconsistent relationship between reward and customers’ e-referral and contributes to related research. Second, the study broadens the existing research perspective by introducing creative self-efficacy, which shows interesting and powerful moderating effect but has been ignored in previous studies. Third, the study provides valuable advice on how enterprises design an effective RRP to enhance customers’ follow-up e-referral.