Search results

The purpose of this paper is to present a new approach to optimizing the design of 3D magnetic circuits. This approach is based on topology optimization, where derivative calculations are performed using the continuous adjoint method. Thus, the continuous adjoint method for magnetostatics has to be developed in 3D and has to be combined with penalization, filtering and homotopy approaches to provide an efficient optimization code.

To provide this new topology optimization code, this study starts from 2D magnetostatic results to perform the sensitivity analysis, and this approach is extended to 3D. From this sensitivity analysis, the continuous adjoint method is derived to compute the gradient of an objective function of a 3D topological optimization design problem. From this result, this design problem is discretized and can then be solved by finite element software. Thus, by adding the solid isotropic material with penalization (SIMP) penalization approach and developing a homotopy-based optimization algorithm, an interesting means for designing 3D magnetic circuits is provided.

In this paper, the 3D continuous adjoint method for magnetostatic problems involving an objective least-squares function is presented. Based on 2D results, new theoretical results for developing sensitivity analysis in 3D taking into account different parameters including the ferromagnetic material, the current density and the magnetization are provided. Then, by discretizing, filtering and penalizing using SIMP approaches, a topology optimization code has been derived to address only the ferromagnetic material parameters. Based on this efficient gradient computation method, a homotopy-based optimization algorithm for solving large-scale 3D design problems is developed.

In this paper, an approach based on topology optimization to solve 3D magnetostatic design problems when an objective least-squares function is involved is proposed. This approach is based on the continuous adjoint method derived for 3D magnetostatic design problems. The effectiveness of this topology optimization code is demonstrated by solving the design of a 3D magnetic circuit with up to 100,000 design variables.

In order to solve the decision-making problem that the attributive weight and attributive value are both interval grey numbers, this paper tries to construct a multi-attribute grey decision-making model based on generalized greyness of interval grey number.

Firstly, according to the nature of the generalized gresness of interval grey number, the generalized weighted greyness distance between interval grey numbers is given, and the transformation relationship between greyness distance and real number distance is analyzed. Then according to the objective function that the square sum of generalized weighted greyness distances from the decision scheme to the best scheme and the worst scheme is the minimum, a multi-attribute grey decision-making model is constructed, and the simplified form of the model is given. Finally, the grey decision-making model proposed in this paper is applied to the evaluation of technological innovation capability of 6 provinces in China to verify the effectiveness of the model.

The results show that the grey decision-making model proposed in this paper has a strict mathematical foundation, clear physical meaning, simple calculation and easy programming. The application example shows that the grey decision model in this paper is feasible and effective. The research results not only enrich the grey system theory, but also provide a new way for the decision-making problem that the attributive weights and attributive values are interval grey numbers.

The decision-making model proposed in this paper does not need to seek the optimal solution of the attributive weight and the attributive value, and can save the decision-making labor and capital investment. The model in this paper is also suitable for the decision-making problem that deals with the coexistence of interval grey numbers and real numbers.

The paper succeeds in realizing the multi-attribute grey decision-making model based on generalized gresness and its simplified forms, which provide a new method for grey decision analysis.

This paper aims to review and assess the sustainable design and management considerations affecting workplaces’ productivity in Saudi Arabia.

The study uses a mixed approach, commencing with literature review, development and pilot-testing of a structured questionnaire instrument to assess design and management considerations towards productively sustainable workplaces (PSWs). In total, 31 considerations affecting sustainable workplaces’ productivity were identified and clustered under five main directions. A collective group of stakeholders, including architects/engineers (A/E), workplace users and facilities managers, were approached to correlate and compare their individual assessment of the identified PSWs. The relative importance index (RII) for each of the PSW considerations is correlated and discussed.

A high level of agreement is recognized among the three stakeholder groups for PSWs considerations’ assessed rankings. The provision of sufficient ventilation and illumination levels, availability of occupational health, security and safety, availability of ergonomically oriented technological infrastructure, availability of formal and informal meeting spaces, efficient space utilization and ergonomic workstations are all ranked highest in importance as design considerations towards PSWs.

The study stems from the need to understand how the PSWs’ design and management considerations are perceived by all streams of stakeholders. The findings identify the considerations’ importance for prioritization on PSWs’ investments.

The best workplaces have been left out from the literature of social sustainability. These companies may cause a significant impact on society given their excellent human resources practices and the employer brand reputation derived from them. This study aims to fill this gap by analysing the social sustainability for the best organisations to work for in Spain.

Using data from an annual ranking for the best workplaces in Spain during 2013–2021, it is proposed to analyse critical social sustainability indicators, comparing organisations within and outside the ranking. Therefore, the authors ask whether companies from the ranking have greater female presence in CEO positions, generate more employment, pay higher salaries and contribute more to the public sector. Methodology comprehends descriptive, exploratory and inference techniques.

Although companies within the ranking achieve a higher score on it when the CEO is female, it does not translate into a greater female CEO presence with respect to companies outside the ranking. On the other hand, best workplaces achieve higher employment rates and pay higher salaries, almost all the time. Also, these excellent companies to work for generate more contributions to the public sector.

This research covers the relation between best human resources practices and social sustainability development, because the former is a great opportunity for pursuing the innovative and long-term policies necessary for the latter. Therefore, findings are valuable for managers and policymakers.

This research delves into a comprehensive examination of Amazon’s Vendor Flex (VF) model, seeking to illuminate the intricacies of supply chain innovation through alliances between Amazon and its suppliers. Employing a multiple case study methodology, the study investigates the reduction of transaction costs, the establishment of strategic alliances for supply chain innovation and governance issues within these alliances.

A multiple case study methodology, incorporating personal interviews and triangulation with primary sources, was employed to unravel the dynamics of the VF model.

Results indicate that the VF model aligns with the reduction of transaction costs by leveraging Amazon’s specialized knowledge, although not necessarily through direct knowledge sharing. Amazon suppliers highlight competitive advantages gained through VF, showcasing efficient navigation of peak seasons and a focus on core activities with online retailing integration. The VF alliance represents a collaborative model where Amazon’s technological prowess enables a streamlined and innovative supply chain for online retailing, which resembles a vertical integration process.

This research underscores the potential of strategic alliances to drive innovation by incorporating industry-leading practices. The governance issues within the VF alliance reveal power imbalances, emphasizing the need for managers to govern dynamics, disclose information and build trust in large-scale alliances.

This study aims to enhance the prediction accuracy of hydroelectricity consumption in China, with a focus on addressing the challenges posed by complex and nonlinear characteristics of the data. A novel grey multivariate prediction model with structural optimization is proposed to overcome the limitations of existing grey forecasting methods.

This paper innovatively introduces fractional order and nonlinear parameter terms to develop a novel fractional multivariate grey prediction model based on the NSGM(1, N) model. The Particle Swarm Optimization algorithm is then utilized to compute the model’s hyperparameters. Subsequently, the proposed model is applied to forecast China’s hydroelectricity consumption and is compared with other models for analysis.

Theoretical derivation results demonstrate that the new model has good compatibility. Empirical results indicate that the FMGM(1, N, a) model outperforms other models in predicting the hydroelectricity consumption of China. This demonstrates the model’s effectiveness in handling complex and nonlinear data, emphasizing its practical applicability.

This paper introduces a scientific and efficient method for forecasting hydroelectricity consumption in China, particularly when confronted with complexity and nonlinearity. The predicted results can provide a solid support for China’s hydroelectricity resource development scheduling and planning.

The primary contribution of this paper is to propose a novel fractional multivariate grey prediction model that can handle nonlinear and complex series more effectively.

Additive Manufacturing technology (AMT) is swiftly gaining prominence to induce automation and innovation in manufacturing systems. It holds immense potential to change supply chain dynamics by providing the possibility of printing objects on demand. This study thus formulates and analyzes the framework to incorporate AMT to handle the spare parts supply chain management (SPSCM) in capital-intensive industries by identifying and assessing the critical success factors (CSFs).

Assessment of the CSFs is performed using the novel Grey Causal Modeling method (GCM) with the objective of making SPSCM resilient and efficient. GCM conducts causal analysis by taking into consideration cause, effects, the objectives, and the situations.

Findings indicate that; Logistics Lead Time (SD4), Time to manufacture (SD3), Management Support (SD11), and Risk Management (SD20) are the most prominent causal factor having a maximum impact when incorporating AMT in SPSCM. The results also reveal that the performance of manufacturing organizations that adopt AMT is substantially influenced by internal and external factors such as Management Support (SD11) and Government Regulations (SD16).

This research provides valuable information for getting the global spare parts supply chain equipped for the post-COVID age, where digital technologies such as AMT will be fundamental for bolstering supply chain resilience and efficiency.

This research proposes a framework for performance assessment when incorporating AMT in SPSCM. Study also demonstrates methodological application of novel Grey Causal Modelling technique using a real case in a spare parts manufacturing industry in India.

Considering the financial service providers’ (FSPs) information asymmetry in evaluating the supplier and their distinct quit probabilities, we want to examine the supplier’s preference of the financing schemes if both the bank and the online platform exist and how the buyer sets the contract terms in the two financing schemes.

We establish a Stackelberg game model to capture the interactions among three parties, i.e. a supplier, a capital-sufficient buyer and an FSP (either a bank or an online platform), within a first-time contract.

In the non-FSPs’ quit case, the buyer’s profit is higher under the bank loan scenario, while the supplier’s profit performs adversely. The supply chain’s profit is heavily dependent on the buyer’s profit difference between the two financing schemes. Moreover, we find that the supplier borrows the money to exactly cover the production cost. The equilibrium solutions of the FSPs’ quit case and of the capital-sufficient supplier’s case are also derived.

First, we assign different risk profiles to different FSPs in our setting so that modeling a previously ignored but practically significant problem. Second, we innovatively take the FSP’s quit probability into account in our model. Third, we elucidate how these factors can influence the relative efficiency of the two types of financing schemes and the settings of the contract, which further complements and extends the current SCF research.

The study examined the relationship between e-learning usage, creativity, e-learning utility and academic performance among undergraduate (UG) and postgraduate (PG) students at Banaras Hindu University (BHU), India.

The data from 480 students were collected using a cross-sectional survey design. Pearson correlation analysis was performed in order to investigate the association between the variables.

The results revealed a significant positive correlation between time of use and academic performance (r = 0.294, p < 0.01). However, there was no significant correlation between time of use and the usability of e-learning in the attainment of learning outcomes. Furthermore, there was a negative correlation between time of use and creativity (r = −0.170, p < 0.01).

The results indicate that educators ought to promote consistent utilization of e-learning platforms to enhance scholarly achievements. However, learners should exercise prudence in their usage, as an over-reliance on these platforms may impede their creativity.

This study contributes to the existing body of knowledge regarding the impacts of e-learning on students' academic performance and creativity. Additionally, it offers instructing professionals and students who make use of e-learning platforms information that might be helpful to them.

The accurate valuation of second-hand vessels has become a prominent subject of interest among investors, necessitating regular impairment tests. Previous literature has predominantly concentrated on inferring a vessel's price through parameter estimation but has overlooked the prediction accuracy. With the increasing adoption of machine learning for pricing physical assets, this paper aims to quantify potential factors in a non-parametric manner. Furthermore, it seeks to evaluate whether the devised method can serve as an efficient means of valuation.

This paper proposes a stacking ensemble approach with add-on feedforward neural networks, taking four tree-driven models as base learners. The proposed method is applied to a training dataset collected from public sources. Then, the performance is assessed on the test dataset and compared with a benchmark model, commonly used in previous studies.

The results on the test dataset indicate that the designed method not only outperforms base learners under statistical metrics but also surpasses the benchmark GAM in terms of accuracy. Notably, 73% of the testing points fall within the less-than-10% error range. The designed method can leverage the predictive power of base learners by incrementally adding a small amount of target value through residuals and harnessing feature engineering capability from neural networks.

This paper marks the pioneering use of the stacking ensemble in vessel pricing within the literature. The impressive performance positions it as an efficient desktop valuation tool for market users.