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The conventional textile dyeing process requires various operational characteristics, and determining the most reliable factor in dyeing performance has always been a challenge for the textile industry. Thus, the present paper aimed to evaluate the process sensitivity of C. I. Reactive Blue 194 dyeing of cotton fabric using a statistical technique.

An L₂₇ orthogonal array-based Taguchi's methodology was used with six parameters and three levels of each parameter. The signal-to-noise (S/N) ratio and analysis of variance were studied using total fixation efficiency (T%) as the response of the process sensitivity.

Results showed that dyebath pH was the most influential factor on the process and total fixation efficiency (p-value = 0.00 and contribution percentage 45.03%), followed by dye-fixing temperature, dye mass, electrolyte concentration, dye-fixing time and material to liquor ratio.

Overall this study provides a foundation for the determination of dyeing process sensitivity that will be useful in textile industries toward further development.

The purpose of this paper is to propose the situation that the existing parking automated guided vehicle (AGV) has a single walking mode, a spin forward motion mode based on a dual steering wheel driven parking AGV. In this way, the AGV can complete the 180° spin of the AGV in the process of straight forward.

A spin forward kinematics model of the dual steering wheel AGV is established, and a motion controller of the dual steering wheel AGV is designed based on the principle of model predictive control to complete the path following the spin forward motion mode.

Computer simulations and laboratory tests were performed on this movement mode, which showed that the operation mode was feasible. It also verified that the mode can improve the handling efficiency, and also solved the problem that the parking space beside the wall could not be set and the site utilization was improved.

The controller should be further improved to make the operation smoother and more accurate.

This mode has the applicability to the indoor logistics AGVs. In addition, it can improve the handling efficiency and also solved the problem that the storage space for goods beside the wall could not be set and the site utilization was improved.

This method can solve the problem due to the increasing number of private cars and parking spaces are hard to find. It increases the number of parking spaces and improves the utilization rate of the site. In addition, it also saves people the time to find a parking space and reduces car exhaust emissions in the process. It follows the requirements of sustainable development.

The studies in this paper provide AGV with more ideas on the issue of improving handling efficiency and site utilization and also solves the problem of being unable to set parking spaces when parking against the wall. In addition, this model has applicability to indoor logistics AGV and plays the same role.

From the intra- and inter-regional dimensions, this paper investigates the linkage between industrial agglomeration and inventory performance, and further demonstrates the moderating role of firm size and enterprise status in the supply chain on this linkage.

Using a large panel dataset of Chinese manufacturers in the Yangtze River Delta for the period from 2008 to 2013, this study employs the method of spatial econometric analysis via a spatial Durbin model (SDM) to examine the effects of industrial agglomeration on inventory performance. Meanwhile, the moderation model is applied to examine the moderating role of two firm-level heterogeneity factors.

At its core, this research demonstrates that industrial agglomeration is associated with the positive change of inventory performance in the adjacent regions, whereas that in the host region as well as in general does not significantly increase. Additionally, both firm size and enterprise status in the supply chain can positively moderate these effects, except for the moderating role of firm size on the positive spillovers.

In view of firm heterogeneity, managers should take special care when matching their abilities of inventory management with the agglomeration effects. Firms with a high level of inventory management are suited to stay in an industrial cluster, while others would be better in the adjacent regions to enhance inventory performance.

This paper is the first to systematically analyze the effects of industrial agglomeration on inventory performance within and across clusters, and confirm that these effects are contingent upon firm size and enterprise status in the supply chain. It adds to the existing literature by highlighting the spatial spillovers from industrial clusters and enriching the antecedents of inventory leanness.

The purpose of this paper is to improve the positioning accuracy of 6-Dof serial robot by the way of error compensation and sensitivity analysis.

In this paper, the Denavit–Hartenberg matrix is used to construct the kinematics models of the robot; the effects from individual joint and several joints on the end effector are estimated by simulation. Then, an error model based on joint clearance is proposed so that the positioning accuracy at any position of joints can be predicted for compensation. Through the simulation of the curve path, the validity of the error compensation model is verified. Finally, the experimental results show that the error compensation method can improve the positioning accuracy of a two joint exoskeleton robot by nearly 76.46%.

Through the analysis of joint error sensitivity, it is found that the first three joints, especially joint 2, contribute a lot to the positioning accuracy of the robot, which provides guidance for the accuracy allocation of the robot. In addition, this paper creatively puts forward the error model based on joint clearance, and the error compensation method which decouples the positioning accuracy into joint errors.

It provides a new idea for error modeling and error compensation of 6-Dof serial robot. Combining sensitivity analysis results with error compensation can effectively improve the positioning accuracy of the robot, and provide convenience for welding robot and other robots that need high positioning accuracy.

This study had a threefold aim: to examine the impact of a Simmelian-tie tripartite alliance on corporate green innovation; to determine the chain-mediating roles of knowledge acquisition and knowledge integration; and to identify the moderating effect of network routines on the relationship between a Simmelian tie and green innovation.

Data were collected through 487 valid survey questionnaires from Chinese small and medium-sized manufacturing enterprises (SMEs). The authors examined the data through a structural model using partial least-squares structural-equation modeling (PLS-SEM) to test the research hypotheses.

The results reveal several key factors with positive impacts on enterprise green innovation. Specifically, a Simmelian tie significantly and positively affects enterprise green innovation. The results further reveal that knowledge acquisition and integration play mediating roles, while a network routine positively moderates the relationships among a Simmelian tie, knowledge acquisition and integration, and corporate green innovation.

This study is among the earliest empirical studies to investigate the influence of Simmelian ties on corporate green innovation for manufacturing companies. This study provides a theoretical basis for managers of firms, especially those of SMEs with limited resources, to fully use Simmelian ties to achieve environmentally sustainable innovation. In addition, this study validates and extends knowledge-management theory by verifying the linking roles of knowledge acquisition and integration and facilitating role of network routines.

The purpose of this paper is to examine how the usage of enterprise social media (ESM) affects eventual employee turnover.

This study developed a theoretical model based on the proposition that different ESM usage behaviors (utilitarian use, hedonic use and social use) have different effects on employee turnover, and job type and job level can moderate the effect of ESM usage on turnover. The model was examined empirically using 1,791 employee samples from a large high-tech manufacturing enterprise deploying ESM.

The results indicate that the utilitarian and social use of ESM has negative effects on turnover, but the hedonic use of ESM has positive effects on turnover. Furthermore, for employees working in different job types and job levels, there are significant differences concerning the effect of ESM usage on their turnover.

ESM managers should encourage employees to use ESM for utilitarian needs and social support but restrict excessive use of ESM for leisure. In addition, different ESM use policies depending upon job types and job levels could be adopted to retain valuable employees.

Few studies have focused on how usage of ESM affects eventual employee turnover. Given the lack of theoretical research and empirical evidence, the authors developed a theoretical model and conducted an empirical study to fill the research gap.

Metaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).

The significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.

The efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.

This paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

Green technology innovation is the organic combination of green development and innovation driven. It is also a powerful guarantee for shaping sustainable competitive advantages of manufacturing enterprises. To explore what kind of executive incentive contracts can truly stimulate green technology innovation, this study aims to distinguish the equity incentive and reputation incentive, upon their contractual elements characteristics and green governance effects, and then put forward suggestions for green technology innovation accordingly.

This study establishes an evaluation model and uses empirical methods to test. Concretely, using data from A-share listed manufacturing companies for the period from 2007 to 2020, this study compares and analyzes the impact of equity and reputation incentive on green technology innovation and explores the relationship between internal green business behavior and external green in depth.

This study finds that reputation incentives focus on long-term and non-utilitarian orientation, which can promote green technology innovation in enterprises. While equity incentives, linked to performance indicators, have a inhibitory effect on green technology innovation. Internal and external institutional factors such as energy conservation measures, the “three wastes” management system, and environmental recognition play the regulatory role in the relationship between incentive contracts and green technology innovation.

Those findings validate and expand the efficient contracting hypothesis and the rent extraction hypothesis from the perspective of green technology innovation and provide useful implications for the design of green governance systems in manufacturing enterprises.

The purpose of this paper is to construct an optimal resource reallocation model of the limited resource by a moderator for reaching the greatest consensus, and show how to reallocate the limited resources by using optimization methodology once the consensus opinion is reached. Moreover, this paper also devotes to theoretically exploring when or what is the condition that the group decision-making (GDM) system is stable; and when new opinions enter into the GDM, how the level of consensus changes.

By minimizing the differences between the individuals’ opinions and the collective consensus opinion, this paper constructs a consensus optimization model and shows that the objective weights of the individuals are actually the optimal solution to this model.

If all individual deviations of the decision makers (DMs) from the consensus balance each other out, the information entropy theorem shows this GDM is most stable, and economically each individual DM gets the same optimal unit of compensation. Once the consensus opinion is determined and each individual opinion of the DMs is under an acceptable consensus level, the consensus is still acceptable even if additional DMs are added, and the moderator’s cost is still no more than a fixed upper limitation.

The optimization model based on acceptable consensus is constructed in this paper, and its economic significance, including the theoretical and practical significance, is emphatically analyzed: it is shown that the weight information of the optimization model carries important economic significance. Besides, some properties of the proposed model are discussed by analyzing its particular solutions: the stability of the consensus system is explored by introducing information entropy theory and variance distribution; in addition, the effect of adding new DMs on the stability of the acceptable consensus system is discussed by analyzing the convergence of consensus level: it is also built up the condition that once the consensus opinion is determined, the consensus degree will not decrease even when additional DMs are added to the GDM.

This study, based on the cognitive dissonance and commitment theories, aims to show that employees with high organizational commitment take more advantage of enterprise social networks (ESNs) due to work-related motivations. Furthermore, this study used the tricomponent attitude model to show that the employees' organizational concern and prosocial values mediate the impact of the organizational commitment on the work-related use of an ESN.

In all, 361 employees from seven Iranian companies using different ESN software packages were surveyed. The validity of the hypotheses was evaluated using partial least square–structural equation modeling (PLS-SEM).

The results of this study confirm that the employees' organizational commitment has a positive impact on their work-related use of the relevant ESN directly and through the mediating roles of their organizational concern and prosocial values.

Previous studies have carefully addressed the role of organizational commitment in the implementation of conventional information systems. However, this is among the few studies addressing the role of commitment in the work-related implementation of ESNs. The results of this study shed light on how employees with a high level of commitment toward the organizations for which they work take advantage of ESNs due to a work-related motivation for the accomplishment of their duties, for bringing benefits into the organization and for helping their coworkers.