Search results

The production planning problem with fine-grained information has hardly been considered in practice. The purpose of this study is to investigate the data-driven production planning problem when a manufacturer can observe historical demand data with high-dimensional mixed-frequency features, which provides fine-grained information.

In this study, a two-step data-driven optimization model is proposed to examine production planning with the exploitation of mixed-frequency demand data is proposed. First, an Unrestricted MIxed DAta Sampling approach is proposed, which imposes Group LASSO Penalty (GP-U-MIDAS). The use of high frequency of massive demand information is analytically justified to significantly improve the predictive ability without sacrificing goodness-of-fit. Then, integrated with the GP-U-MIDAS approach, the authors develop a multiperiod production planning model with a rolling cycle. The performance is evaluated by forecasting outcomes, production planning decisions, service levels and total cost.

Numerical results show that the key variables influencing market demand can be completely recognized through the GP-U-MIDAS approach; in particular, the selected accuracy of crucial features exceeds 92%. Furthermore, the proposed approach performs well regarding both in-sample fitting and out-of-sample forecasting throughout most of the horizons. Taking the total cost and service level obtained under the actual demand as the benchmark, the mean values of both the service level and total cost differences are reduced. The mean deviations of the service level and total cost are reduced to less than 2.4%. This indicates that when faced with fluctuating demand, the manufacturer can adopt the proposed model to effectively manage total costs and experience an enhanced service level.

Compared with previous studies, the authors develop a two-step data-driven optimization model by directly incorporating a potentially large number of features; the model can help manufacturers effectively identify the key features of market demand, improve the accuracy of demand estimations and make informed production decisions. Moreover, demand forecasting and optimal production decisions behave robustly with shifting demand and different cost structures, which can provide manufacturers an excellent method for solving production planning problems under demand uncertainty.

As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

This article develops an innovative multidisciplinary conceptual framework in the field of refugee entrepreneurship by combining the theory of mixed embeddedness with the concepts of intersectionality and agency. Focusing on the phenomenon of refugee entrepreneurship, this conceptual framework addresses the following questions: how is entrepreneurship informed by the various intersectional positions of refugees? And how do refugees exert their agency based on these intersecting identities?

By revising the mixed embeddedness approach and combining it with an intersectional approach, this study aims to develop a multidimensional conceptual framework.

This research illustrates how the intersectional positions of refugees impact their entrepreneurial motivations, resources and strategies. The authors' findings show that refugee entrepreneurship not only contributes to the economic independence of refugees in new societies but also creates opportunities for refugees to exert their agency.

This conceptual framework can be applied in empirical research and accordingly contributes to refugee entrepreneurship studies and intersectionality theory.

Hydro-viscous drive (HVD) clutches are widely used in equipment requiring soft start, such as fans and pumps, to transmit torque and adjust speed by changing the gap distance between friction pairs. This paper aims to propose a novel two-parameter evaluation method for HVD during the mixed lubrication stage. The objective is to develop an effective model that establishes the relationship between these parameters and the actual surface topography.

In the presented methods, the fractal features of the real manufacturing surface are calculated based on the power spectrum function by the ultra-depth three-dimensional microscope. After that, the hybrid friction model of the friction plate is established based on mixed elasto-hydrodynamic lubrication theory, boundary friction model and fractal theory. Then the torque and load bearing characteristics of the clutch are obtained, and the influences of the surface fractal features are investigated and discussed. Finally, the Weierstrass–Mandelbrot function is adopted for the surface topography characterization and evaluation.

The results indicate that the proposed method exhibits good accuracy, while the speed difference between the friction pair exceeds 2,500 rpm. It is concluded that this paper proposed a way to evaluate the torque and loading capacity of HVD considering the real manufacturing surface topography and is helpful for surface optimization.

The originality and value of this study lie in its development of a novel torque and load bearing capacity evaluation method for HVD in mixed lubrication stage, considering manufacturing surface topography and describing the real manufacturing surface.

The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.

This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.

The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.

Large computational cost.

Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.

Simplify the setting of boundary conditions in the transient calculation.

Both issuing firms and underwriters shall benefit from the associations in underwriting contracts for seasoned equity offerings (SEOs). Issuing firms that are offered underwriting contracts with clustered gross spreads do not have strong incentives to switch away from the firms' prior SEO underwriters, and thus these existing underwriters are able to maintain or gain greater market share. This study investigates how the clustering of percentage gross spreads affects the likelihood of underwriter switching.

Using the investment bank-underwritten SEOs in Hong Kong, the authors find that the percentage gross spreads of 40% of these SEOs are clustered at 2.5%. The seemingly unrelated bivariate probit model, Weibull survival mixed model and trivariate probit model are applied to analyse this phenomenon.

The authors' study provides first direct evidence that the clustering of percentage gross spreads lowers the likelihood of underwriter switching. Investment banks as underwriters can explicitly price underwriting contracts at a clustered level, more likely in periods of greater market volatility, and intentionally retain the banks' client firms using pricing arrangements. The authors' finding and approach offer more direct and distinct support that the issuer–underwriter association can be relationship-based.

Whilst the clustering of fees is interpreted as a type of anticompetitive price sitting, the authors contribute to literature by providing new empirical evidence on why percentage gross spreads as a price dimension are clustered. On top of contract efficiency and collusion, this study's new evidence provides a third view for the clustering of gross spreads.

This interdisciplinary study aims to analyze how service organizations communicate sustainable beliefs in their social media narratives and use them to generate brand awareness, customer recognition and ongoing demand for sustainable service.

A two-phase exploratory analysis of 10,342 tweets from 2019–2020 was conducted by sustainable global corporations to identify best practices for their social media teams operating within a service-based business model. First, the significant themes were identified using an unguided machine learning approach of three types of firms: services, goods and mixed. Next, the full set of tweets with linguistic sentiment analysis was analyzed followed by a deeper view of the services-based organizations based on their strategic focus (business-to-business [B2B] versus mixed).

The findings indicate that tweets that appear to create the highest customer engagement are characterized as having high levels of analytical language, high clout (i.e. are socially relevant), a positive tone, a high number of words and a high number of words per sentence. On the other hand, having complex language in terms of six-letter words does not seem to associate with customer engagement. The last level of analysis shows that B2B services-based corporations with positive tone and higher word count exhibit higher levels of retweets. Implications include providing rational and informational tweets to increase engagement and highlight societal relevance.

Climate change has negative consequences on human and physical capital, and ecosystems across the globe. This study provides specific recommendations for how services corporations can increase their sustainable communications and actions.

The key implication of our research is that corporations must strategically design social media narratives about climate change as part of their online branding and communications process.

This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

This paper aims to introduce a heteroskedastic hidden truncation normal (HTN) model that allows for conditional volatilities, skewness and kurtosis, which evolve over time and are linked to economic dynamics and have economic interpretations.

The model consists of the HTN distribution introduced by Arnold et al. (1993) coupled with the NGARCH type (Engle and Ng, 1993). The HTN distribution nests two well-known distributions: the skew-normal family (Azzalini, 1985) and the normal distributions. The HTN family of distributions depends on a hidden truncation and has four parameters having economic interpretations in terms of conditional volatilities, kurtosis and correlations between the observed variable and the hidden truncated variable.

The model parameters are estimated using the maximum likelihood estimator. An empirical application to market data indicates the HTN-NGARCH model captures stylized facts manifested in financial market data, specifically volatility clustering, leverage effect, conditional skewness and kurtosis. The authors also compare the performance of the HTN-NGARCH model to the mixed normal (MN) heteroskedastic MN-NGARCH model.

The paper presents a structure dynamic, allowing us to explore the volatility spillover between the observed and the hidden truncated variable. The conditional volatilities and skewness have the ability at modeling persistence in volatilities and the leverage effects as well as conditional kurtosis of the S&P 500 index.