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The purpose of this paper is to review systematically the research of grey relation analysis (GRA) models.

Three different approaches, the springboard to build a GRA model, the angle of view in modelling, and the dimension of objects, are analysed, respectively.

The GRA models developed from the models based on relation coefficients of each point in the sequences in early days to the generalized GRA models based on integral or overall perspective. It evolved from the GRA models which measure similarity based on nearness, into the models which consider similarity and nearness, respectively. The objects of the research advanced from the analysis of relationship among curves to that among curved surfaces, and further to the analysis of relationship in three‐dimensional space and even the relationship among super surfaces in n‐dimensional space.

The further research on GRA models is proposed. One is about the property of GRA model. An in‐depth knowledge about the properties of GRA model will help people to understand its function, applicable area and requirements for modelling. The other one is about the extension of research object system. The object to be analysed should be extended from the common sequence of real numbers to grey numbers, vectors, matrices, and even multi‐dimensional matrices, etc.

The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

By construction, income inequality measures employed in well-being analysis presume all individual differences to be deleterious to the social good. Yet some differences, for example, those acceptable to all and necessary for optimal resource allocation in producing that well-being, are demonstrably beneficial. Measured inequality is an amalgam of both deleterious or ‘Bad’ and beneficial or ‘Good’ differences, and from both policy and well-being measurement perspectives, distinguishing between types with measures fit for purpose makes sense, especially if the types are taking different paths. Here, as an exemplar, the distinction is explored in considering the progress of human resource, gender, and immigrant status-based personal income differences in twenty-first-century Canada. Categorising human resource-based differences as efficiency promoting ‘Good’ inequalities and gender and immigrant status-based differences as discriminatory and ‘Bad’ reveals that, under all proposed measures, while aggregate and ‘Good’ inequality grew over the sample period, ‘Bad’ inequality diminished, reinforcing the case for inequality measures that are fit for purpose.

The purpose of this paper is to investigate the performance of unpaced unreliable production lines that are deliberately unbalanced in terms of their coefficients of variation (CVs).

A series of simulation experiments were carried out for five and eight station lines with mean buffer space set at one, two, four and six units. CVs were allocated in 12 different configurations for each of these lines.

The results show that the best unbalanced CV patterns in terms of throughput rates or idle times as compared to a balanced line counterpart are those where the steadiest stations are concentrated near the centre of the line. On the other hand, either concentrating the steadier operators towards the centre or close to the end of the line gives best average buffer level results.

The results provide guidelines for production line managers when designing unpaced unbalanced lines depending on their performance aims.

The investigation of the effects of unbalancing CVs in unreliable lines has not previously been studied and can provide insights into how best to place workstations with differing variability along the line.

Addressing occupational stress and fostering employee wellness helps meet a host of organizational stakeholder expectations including high quality of work life (employees), reasonable return on investment (investors), increased productivity (management), and competitiveness (owners). Despite being dynamic in nature, stress and wellness are often studied using a static perspective. One reason for the scarcity of dynamic empirical research is the limited knowledge and use of the tools available to assess change over time. To address this limitation, four tools used to assess change and dynamics of occupational stress and well-being are described: growth models, latent change score models, spectral analysis, and computational modeling. First, we begin by discussing growth curve models and then transition to latent change score models. We then expand into spectral analysis, a tool used to determine cycles of ups and downs that repeat regularly. Last, computational modeling is discussed, where computers and simulations are used to understand a dynamic process. For each tool, we give examples of how they have been used, make recommendations for future use, and provide readers with suggestions and references for how to complete analyses in software and programs, most of which are freely available (i.e., R, Vensim).

Corruption is a wicked issue affecting public sector organizations across the world. Even though research in this field is blooming, little is known about the strategies that could be implemented to prevent and/or fight the occurrence of public corruption. The purpose of this paper is to fill the extant gaps in the scientific knowledge, providing insights into the strategies that can be use to fight public corruption.

Using a longitudinal approach, the paper points out some evidence on the perceived effectiveness of different public governance initiatives in reducing the risks of public corruption in Europe. More specifically, 31 Western European countries were involved in this analysis. The research covered a seven years’ time-span, ranging from January 2010 to December 2016.

Rule of law (RL) was found to be an important ingredient of the recipe for successful anti-corruption policies. Conversely, political stability (PS) turned out to entail greater incentives to public corruption. Whilst voice and accountability, government effectiveness, regulatory quality and control of corruption were correlates of perceived anti-corruption performance, they were not found to act as relevant regressors in the panel model.

The generalizability of the research results is limited by the geographical boundaries of this study; besides, there is the risk that the study findings were affected by the consequences of the recent financial turbulences concerning Western European countries.

RL is momentous to realize the full potential of anti-corruption policies: in fact, it contributes in reducing the incentives to improperly use public assets, enhancing the proper functioning of public sector organizations. Alternatively, PS may induce public officials and citizens to collude in order to capture public resources.

The paper contributes in shedding light on the perceived effectiveness of anti-corruption initiatives, paving the way for further conceptual and practical developments.

Online knowledge integration has been an important concern of the online knowledge community as it can lead to various positive outcomes of online knowledge coproduction. This paper identifies online knowledge integration factors by considering group heterogeneity and group interaction process.

Based on the categorization-elaboration model (CEM) and interactive team cognition (ITC) theory, a research model that reflects the antecedent's factors and mediating factors of online knowledge integration was developed and empirically examined based on data collected from 2,339,836 data extracted from Wikipedia.

Group interaction process plays an essential mediator role in online knowledge integration. Group knowledge heterogeneity negatively influences online knowledge integration and group experience heterogeneity positively, and they both positively promote online knowledge integration through group interaction process with different paths.

Our research concerns the OKC context in one setting (Wikipedia). We expect that the results will generalize to other OKC platforms.

The findings of the study could assist the online knowledge community's organizers to understand the motivational mechanisms of online knowledge integration. Group interaction process could be regarded as the key role to promote group wisdom and maintain group independence.

We advance the understanding of the online knowledge integration and gain a richer understanding of the importance of group interaction independence for online knowledge integration based on the agreement of group wisdom. It suggested keeping group interaction independence is an important aspect for highly online knowledge integration among heterogeneity groups.

This study extends CEM and ITC theory to the domain of knowledge integration context and finds the mechanism between group heterogeneity and online knowledge integration by introducing the group interaction process.

The purpose of this research is to examine the impact of audit committee financial experts on the risk of financial corruption in public companies.

A time-lagged, matched-pairs sample of 352 corporations was utilized to test the study's hypotheses (176 financially corrupt firms plus 176 compliant firms). To uncover financially corrupt firms, 2,895 Accounting and Auditing Enforcement Releases from the Securities and Exchange Commission were thoroughly evaluated.

The results show that financial experts on audit committees generally increased financial corruption. However, the impact was reversed when audit committees had three or more financial experts, showing that having at least three financial experts reduced financial corruption.

The study's findings call into question the long-held practice of appointing at least one financial expert to audit committees. This study offers a novel approach to improve corporate oversight and reduce financial corruption by having at least three financial experts on audit committees.

The purpose of this paper is to propose variants of an adaptive penalty scheme for steady-state genetic algorithms applied to constrained engineering optimization problems.

For each constraint a penalty parameter is adaptively computed along the evolution according to information extracted from the current population such as the existence of feasible individuals and the level of violation of each constraint. The adaptive penalty method (APM), as originally proposed, computes the constraint violations of the initial population, and updates the penalty coefficient of each constraint after a given number of new individuals are inserted in the population. A second variant, called sporadic APM with constraint violation accumulation, works by accumulating the constraint violations during a given insertion of new offspring into the population, updating the penalty coefficients, and fixing the penalty coefficients for the next generations. The APM with monotonic penalty coefficients is the third variation, where the penalty coefficients are calculated as in the original method, but no penalty coefficient is allowed to have its value reduced along the evolutionary process. Finally, the penalty coefficients are defined by using a weighted average between the current value of a coefficient and the new value predicted by the method. This variant is called the APM with damping.

The paper checks new variants of an APM for evolutionary algorithms; variants of an APM, for a steady-state genetic algorithm based on an APM for a generational genetic algorithm, largely used in the literature previously proposed by two co-authors of this manuscript; good performance of the proposed APM in comparison with other techniques found in the literature; innovative and general strategies to handle constraints in the field of evolutionary computation.

The proposed algorithm has no limitations and can be applied in a large number of evolutionary algorithms used to solve constrained optimization problems.

The proposed algorithm can be used to solve real world problems in engineering as can be viewed in the references, presented in this manuscript, that use the original (APM) strategy. The performance of these variants is examined using benchmark problems of mechanical and structural engineering frequently discussed in the literature.

It is the first extended analysis of the variants of the APM submitted for possible publication in the literature, applied to real world engineering optimization problems.