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This research aims to focus on the problem of optimizing the layout of charging stations.

The authors estimate the total number of charging stations by the number of existing gas stations using clustering by fast search-and-find of density peaks (CFSFDP) algorithm.

This paper reveals the relationship between the distance and the population. Priority should be given to the short-distance and high-frequency travel charging needs in the city, followed by low frequency and travel, which can improve the use efficiency of the whole charging network.

First, the total number of charging stations is estimated by the number of existing gas stations. This method can fully consider the growth potential of charging vehicles instead of the traditional cars. Besides, the cost-benefit method is proposed to explore the marginal coverage distance of the city’s charging network and can be used to reveal the relationship between the distance and the population.

Advanced manufacturing machines (AMMs) are searched as a momentous asset across the manufacturing societies for quenching and addressing the production units under economical circumstances, i.e. production of high-quality of goods under feasible cost. AMMs are significant in holding the managers against their rivals and competitors with high profit margins. The authors developed the decision support mechanism/portfolio (DSM-P) consist of knowledge-based cluster approach with a dynamic model. The purpose of research work is to measure overall economic worth of AMMs under objective and grey-imperfect (mixed) data by exploring the proposed DSM-P.

The authors developed the DSM-P that consist of knowledge-based cluster, three multi-criteria decision-making (MCDM) techniques-1-2-3 with complementary grey relational analysis-4(GRA), approach with a dynamic model (complied by technical plus cost and agility measures of AMMs). The proposed DSM-P enables the manager to map the overall economic worth of candidate AMMs under objective and grey-mixed data.

The presented DSM-P assist the managers for handling the selection problem of AMMs, i.e. CNCs, robots, automatic-guided vehicle, etc under mixed (objective cum grey) data. To enable the readers for intensely understand the work, the utility of proposed approach is displayed by illustrating a polar robot evaluation and selection problem. It is ascertained that the robot candidate-11 alternative is fulfilling the entire technical cum cost and agility measures.

The DSM-P provides more precise and reliable outcomes due to a usage of the dominance theory. Under the dominance theory, the ranks are obtained by MCDM techniques-1-2-3 are compared with ranks gathered by the GRA-4 under objective cum grey data, formed the novelties in presented research work. From a future perspective, the grey-based models in DSM-P can be built/extended/constructed more extensive and can be simulated by the same approach.

Marking and inspecting are key steps in hull structure construction. However, currently most marking and inspecting operations are still carried out manually, which leads to low assembly efficiency and quality. This paper aims to solve these problems through the application of digital technology: the optical projection and machine vision.

First, the assembly process model of hull construction is established in 3D design environment. Second, the process information is presented to workers in a virtual form through optical projector, which provides accurate guidance for the manual operation. On this basis, the workers can complete welding and assembly operations readily. Finally, the machine vision method is used to check the assembly results, which can decrease the subjective errors.

A rapid and accurate assembly positioning for hull structure construction is realized based on optical projection, which can avoid the pollution caused by the marking machine and the error caused by human.

This paper combines the advantages of optical projection and machine vision to the field of shipbuilding. The shortcomings of the traditional marking and inspection methods is effectively solved, which may provide a new way for enhancing the assembly efficiency and quality.

The purpose of this study is to identify a comprehensive list of main enablers of successful implementation of green manufacturing (GM) practices in small and medium-sized enterprises (SMEs). Based on the inputs from Irish SMEs' experts, a validated conceptual hierarchy model of the identified enablers is developed to analyse and prioritise the most dominant ones.

Data were collected using a questionnaire survey responded by various professionals in Irish manufacturing SMEs. Experts' opinions were analysed using interpretive structural modelling (ISM) and cross-impact matrix multiplication applied to classification (MICMAC) to distinguish the mutual influences amongst the nine identified enablers.

The findings revealed that from the Irish SMEs' perspective, strong green supply chain relations are the outcome of successfully implementing GM and not a driver of the other enablers. Furthermore, lower manufacturing costs are found to arise as a result of GM practices in SMEs.

It is discussed that emulating the actions of larger companies will not necessarily yield the same results for SMEs. Innovation will become a cornerstone of remaining competitive in a sustainable environment, which is facilitated by closer green supply chain relations.

The reported findings in this article contribute to theory and practice of GM and SMEs research domains by systematically identified, classified and ranked enablers in a conceptual hierarchal model from the perspective of Irish SMEs. This research lays the foundations for further analysis of the GM practices enablers in a time when UN mandates and EU regulations are increasingly impacting SMEs all around Europe and especially Ireland.

Circular economy denotes future sustainability that allows optimum utilization of resources. In the present era of technology, plenty of innovations are happening across the world, and digital manufacturing is one of such innovations. However, there are several barriers which are impeding adoption of digital manufacturing in circular economy environment. The study explores the barriers of digital manufacturing initiatives in a circular economy and develops a methodological model to prioritize the identified challenges for automotive parts manufacturing industry.

Seven categories of challenges namely process, human resources, financial, collaboration, technological, security and leadership challenges were identified from literature and further validated with subsequent discussions with experts from the industry. The study is conducted in two phases, where in the first phase, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique is used to define the priority and importance of seven categories of challenges. In second phase, the barriers are ranked using a Fuzzy Performance Important Index (FPII), taking into account contextual factors associated with the challenges and linked barriers, to determine the extent to which they impede the adoption of digital manufacturing in the sample automotive parts manufacturing company.

The “risk of data security and information privacy in connection with use of external data and protecting customer data” appeared as the most significant barrier to digital manufacturing in circular economy. Furthermore, technological challenges emerged as the most significant category of challenges followed by financial challenges in adoption of digital manufacturing in circular economy.

Identification of the identified barriers and understanding the interrelationships will lead to easier adoption of digital manufacturing in circular economy.

Despite all the potential benefits of implementing Industry 4.0 technologies in manufacturing industries, the adoption thereof is still in nascent phase with significant challenges yet to be overcome to accelerate the pace of adoption. Hence, this study explores the barriers preventing companies from adopting and benefiting from digital manufacturing initiatives and further develops a methodological model.

The purpose of this study is to explore the green innovation evolution in coal enterprises in China and the various driving mechanisms influenced by government regulations, enterprise resource capability and supply chain integration.

This paper uses the innovation data of coal enterprises in China for constructing a model to infer how enterprises achieve green innovation (the “hybrid drive effect model”).

This paper shows that various driving modes of green innovation in an enterprise are influenced by government regulations, enterprise resource and capability, supply chain integration and the interaction effects between any two actors.

By examining the evolution process of green innovation in a specific industry from the perspectives of exploitative and exploratory innovation, the paper touches a relatively novel view on green innovation.

The aim of this study is to determine the impact of different dimensions of consumer engagement in social media on firm performance in social media.

Beyond likes and comments, combined with the interactive behavior of consumer engagement with firms on social media, we proposed a set of metrics for consumer engagement on social media, namely personal engagement, user-interactive engagement, and fan-interactive engagement. We used the fixed-effect model and validated the impact of consumer engagement in different dimensions on firm performance.

The result shows that personal engagement and user-interactive engagement significantly correlate with firm performance; however, the same effect was not observed in fan-interactive engagement.

Prior researches on consumer engagement in social media are mainly about consumer engagement in firm-initiated social media page. In this study, we consider the consumer engagement behavior in firm-initiated and consumer-initiated social media pages, then validate the impact of different dimensions of consumer engagement on firm performance.

The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).

A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.

The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.

There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.

This paper aims to present a simplified solution method for the elasto-plastic consolidation problem under different stress paths.

First, a double-yield-surface model is introduced as the constitutive model framework, and a partial derivative coefficient sequence is obtained by using numerical approximation using Gauss nuclear function to construct a discretization constitutive model which can reflect the influence of different stress paths. Then, the model is introduced to Biot’s consolidation theory. Volumetric strain of each step as the right-hand term, the continuity equation is simplified as a Poisson equation and the fundamental solution is derived by the variable separation method. Based on it, a semi-analytical and semi-numerical method is presented and implemented in a finite element program.

The method is a simplified solution that is more convenient than traditional coupling stiffness matrix method. Moreover, the consolidation of the semi-infinite foundation model is analyzed. It is shown that the numerical method is sufficiently stable and can reflect the influence of stress path, loading distribution width and some other factors on the deformation of soil skeleton and pore water pressure.

Original features of this research include semi-numerical semi-analytical consolidation method; pore water pressure and settlements of different stress paths are different; maximum surface uplift at 3.5a; and stress path is the main influence factor for settlement when loading width a > 10 m.

Public hospitals in China usually rely on revenues from medical services and medications to compensate for major costs given their nonprofit nature. The lack of government subsidies and unreasonable prices of medical services have led to high medical costs and unbalanced reimbursement system for public hospitals. There is a critical need of research on improvement of reimbursement system that will create positive effect on China’s health-care system. This paper aims to focus on four dimensions of stakeholders (government, patients, medical insurance agencies and social organization) and six major expenditures to explore reimbursement scheme for public hospitals in China with the purpose of relieving unbalanced income and expenditure of hospitals, avoiding medication markups and reducing medical expenses from patients.

In this paper, the authors study reimbursement scheme for public hospitals from the perspective of four dimensions of stakeholders and how stakeholders reimburse six major expenditures of hospitals. A total of 128 effective samples were collected from financial data of 32 public hospitals through 2009-2012. This paper analyzes the econometric models of the selected revenue and expenditure. This paper analyzes the econometric models of the selected revenue and expenditure using linear regression. The linear relationship between each cost and different types of incomes (i.e. reimbursements from government, patients, insurance agencies and social organization) is analyzed before and after cancelling the medication markups.

Results from empirical research verify that government reimbursement is insufficient, and using medication markups to compensate for medical service costs has become a serious problem for China’s public hospitals. To avoid the medication markups and improve the reimbursement scheme, government should reimburse labor cost, fixed assets cost and research cost; patients and medical insurance agencies should reimburse the costs of medical service, medication and administration/operations; and social organization should supplement the fixed assets cost.

In this study, the authors defined and classified stakeholders of reimbursement scheme for public hospitals in China, which help understand the roles and effects that different stakeholders can play in compensation. Along with the proposed reimbursement scheme framework, this study will help make effective implementation of new health-care reform program in China.