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Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

Previous studies have examined the empowerment of individual teachers, while neglecting the fact that such a phenomenon might grow within a team. Building on the crossover model and social exchange theory, the aim of this study is to explore whether team empowerment among school management teams (SMTs), is transmitted to the school level and affects schoolteachers' job satisfaction and thereby schoolteachers' organizational citizenship behavior (OCB). Furthermore, we explored whether those relationships are moderated by team-member exchange (TMX) relationships.

Data were collected from 86 principals, 357 SMT members and 683 schoolteachers from 86 schools.

Results confirmed the mediating role of schoolteachers' job satisfaction, showing a positive relationship between SMT empowerment and schoolteachers' job satisfaction, and between job satisfaction and OCB. The moderation of TMX and the overall moderated mediation hypotheses were not supported.

The nature of data collected in the current study precludes any inference concerning the direction of casual links among the study constructs. Therefore, longitudinal studies could be designed, aimed at confirming the direction of links among the variables.

The findings reinforce the impact of schoolteachers' job satisfaction on achieving OCBs. Hence, SMT members carry the responsibility to cultivate satisfied schoolteachers through schools' support mechanisms and guidance in order to achieve schoolteachers' OCB.

The study identifies SMT empowerment as a key factor that may indirectly encourage schoolteachers to invest in OCBs through positive attitudes of schoolteachers' job satisfaction.

This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops.

In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions.

The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop.

In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

Considering the conservation of resources (COR) theory, this research investigates the mediating roles of work, family and culture on the relationship between employee corporate social responsibility (CSR) and employee well-being.

Self-administered questionnaire data from 403 employees working across multiple organisations in the United Kingdom were analysed using path analysis with SmartPLS.

Organisations can only maximally benefit from their CSR investment when specific HR strategies are in place, as no direct relationship between CSR and well-being outcomes was observed. Family-to-work spillover and work–family culture were significant mediators in the relationship between employee CSR and well-being.

CSR strategies targeted at improving employee well-being do not necessarily do so in a direct approach. They, therefore, may not serve the desired performance outcomes of organisations. Boundary conditions of applying the crossover model of COR theory were observed.

This research contributes to the limited knowledge of the effectiveness of employee-related CSR strategies on HRM and well-being outcomes from an employees' perspective. Employee-related CSR strategies are unlikely to improve employee well-being if the inter-individual level of analysis, i.e. interchange between work, family and culture, is not considered.

This study aims to explore how subsidiary capabilities collectively configure for performance. Additionally, it seeks to examine whether these configurations of capabilities can provide equifinal solutions through developing a comprehensive research framework that focuses on subsidiaries in China.

With a data set collected through a questionnaire from 172 Korean multinational enterprises (MNEs) in China, this study used a fuzzy-set qualitative comparative analysis to detect the capability conditions and configurations. These configurations represent combinations of various subsidiary capabilities linked to high performance.

This study identified several complex pathways with distinct configurations for high subsidiary performance. The findings demonstrate the importance of configurations over individual conditions. Thus, the results highlight that the effectiveness of diverse capabilities, which are widely believed to singularly contribute to the high performance of MNE subsidiaries, depends on how each combines with other capabilities. Overall, the findings provide a richer and fine-grained understanding of the role and relative importance of various forms of MNE subsidiary capabilities and how the joint effect of these subsidiaries contributes to high performance.

This study suggests that MNE managers should comprehensively understand how subsidiary capabilities are configured to produce subsidiary performance outcomes. This specifically illustrates the importance of understanding the mutually conflicting yet collectively exhaustive results of multi-selective solutions and aims to align with China’s industrial and regional heterogeneity.

By examining the role of MNE subsidiary capability configurations, which may collectively influence the subsidiary’s performance, this study contributes to the literature. It elucidates how MNE subsidiaries may achieve superior performance by developing and possessing various capabilities tailored to the local context.

The interruption event will seriously affect the normal operation of urban rail transit lines,causing a large number of passengers to be stranded in the station and even making the train stranded in the interval between stations. This study aims to reduce the impact of interrupt events and improve service levels.

To address this issue, this paper considers the constraints of train operation safety, capacity and dynamic passenger flow demand. It proposes a method for adjusting small loops during interruption events and constructs a train operation adjustment model with the objective of minimizing the total passenger waiting time. This model enables the rapid development of train operation plans in interruption scenarios, coordinating train scheduling and line resources to minimize passenger travel time and mitigate the impact of interruptions. Regarding the proposed train operation adjustment model, an improved genetic algorithm (GA) is designed to solve it.

The model and algorithm are applied to a case study of interruption events on Beijing Subway Line 5. The results indicate that after solving the constructed model, the train departure intervals can be maintained between 1.5 min and 3 min. This ensures both the safety of train operations on the line and a good match with passengers’ travel demands, effectively reducing the total passenger waiting time and improving the service level of the urban rail transit system during interruptions. Compared to the GA algorithm, the algorithm proposed in this paper demonstrates faster convergence speed and better computational results.

This study explicitly outlines the adjustment method of using short-turn operation during operational interruptions, with train departure times and station stop times as decision variables. It takes into full consideration safety constraints on train operations, train capacity constraints and dynamic passenger demand. It has constructed a train schedule optimization model with the goal of minimizing the total waiting time for all passengers in the system.

This study aims to examine the effect of job insecurity and perceived work-social support on career optimism, organizational commitment and turnover intention in the hospitality industry in the post-COVID era.

Data collection was via an online survey of 428 hotel staff. Structural equation modeling techniques support the research model and hypothesis testing.

This study reveals that the perception of job insecurity has a strong effect on turnover intention but no significant influence on career optimism and organizational commitment. Perceived work social support plays a vital role in employees’ coping strategies in difficult work circumstances. It has positive effects on career optimism and organizational commitment.

The study is considered timely in verifying how the perception of job insecurity and work social support influence hospitality employees’ career optimism, organizational commitment and turnover intention as we transition to the post-pandemic era. The findings enrich the literature on job insecurity and career management through a crisis.

本研究旨在研究后COVID-19时期工作不稳定（JI）和工作支持（PS）的认识对酒店业职业乐观OP）、组织承诺（OC）与离职意向（TI）的影响。

数据是通过对428名酒店员工进行在线调查收集的。结构方程模型分析技术用于检验模型及研究假设。

本研究表明JI的认识对TI的影响具有统计意义, 但对OP和OC并没有显著影响。PS的认识对员工在困难工作环境中的应付策略起着重要作用。PS对员工的OP及OC有着积极的影响。

该研究可以说是非常及时地阐明了JI和PS的认识对后疫情时期的酒店业员工的OP、OC与TI的影响。研究结果有助于丰富人们对危机中的JI以及职业管理的知识。

Este estudio examina el efecto de la inseguridad laboral y el apoyo social percibido en el trabajo sobre el optimismo profesional, compromiso organizativo y la intención de rotación en la industria hotelera en la era post-COVID.

La recogida de datos se realizó mediante una encuesta en línea a 428 empleados de hostelería. El modelo de investigación y la comprobación de hipótesis se ha realizado mediante la modelización de ecuaciones estructurales.

Este estudio revela que la percepción de inseguridad laboral tiene un fuerte efecto en la intención de rotación, pero no influye significativamente en el optimismo profesional y el compromiso organizativo. El apoyo social percibido en el trabajo desempeña un papel vital en las estrategias de afrontamiento de los empleados en circunstancias laborales difíciles. Tiene efectos positivos sobre el optimismo profesional y el compromiso organizativo.

El estudio se considera oportuno para verificar cómo la percepción de la inseguridad laboral y el apoyo social en el trabajo influyen en el optimismo profesional, el compromiso organizativo y la intención de cambio de los empleados de hostelería en la transición a la era pospandémica. Los resultados enriquecen la literatura sobre la inseguridad laboral y la gestión de la carrera profesional en situaciones de crisis.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

This study aims to reduce the redundant weight of the anti-roll torsion bar brought by the traditional empirical design and improving its strength and stiffness.

Based on the finite element approach coupled with the improved beluga whale optimization (IBWO) algorithm, a collaborative optimization method is suggested to optimize the design of the anti-roll torsion bar structure and weight. The dimensions and material properties of the torsion bar were defined as random variables, and the torsion bar's mass and strength were investigated using finite elements. Then, chaotic mapping and differential evolution (DE) operators are introduced to improve the beluga whale optimization (BWO) algorithm and run case studies.

The findings demonstrate that the IBWO has superior solution set distribution uniformity, convergence speed, solution correctness and stability than the BWO. The IBWO algorithm is used to optimize the anti-roll torsion bar design. The error between the optimization and finite element simulation results was less than 1%. The weight of the optimized anti-roll torsion bar was lessened by 4%, the maximum stress was reduced by 35% and the stiffness was increased by 1.9%.

The study provides a methodological reference for the simulation optimization process of the lateral anti-roll torsion bar.

Vertical electrical sounding (VES) and Rayleigh wave exploration are widely used in the exploration of near-surface structure, but both have limitations. This study aims to make full use of the advantages of the two methods, reduce the multiple solutions of single inversion and improve the accuracy of the inversion. Thus, a nonlinear joint inversion method of VES and Rayleigh wave exploration based on improved differential evolution (DE) algorithm was proposed.

Based on the DE algorithm, a new initialization strategy was proposed. Then, taking AK-type with high-velocity interlayer model and HA-type with low-velocity interlayer model near the surface as examples, the inversion results of different methods were compared and analyzed. Then, the proposed method was applied to the field data in Chengde, Hebei Province, China. The stratum structure was accurately depicted and verified by drilling.

The synthetic data and field data results showed that the joint inversion of VES and Rayleigh wave data based on the improved DE algorithm can effectively improve the interpretation accuracy of the single-method inversion and had strong stability and large generalizable ability in near-surface engineering problems.

A joint inversion method of VES and Rayleigh wave data based on improved DE algorithm is proposed, which can improve the accuracy of single-method inversion.