Jianguo Zheng’s research while affiliated with Donghua University and other places

For real-world production environments, few studies consider urgent job insertion in a distributed hybrid flowshop scheduling problem (DHFSP). Thus, a variant of DHFSP called the distributed hybrid flowshop rescheduling problem (DHFRP) with sequence-dependent set-up time and transportation time is investigated. Combining the characteristics of DHFRP, a mathematical model is constructed, and a Q-learning grey wolf optimizer (QGWO) is proposed to handle it. In QGWO, combining two hybrid initialization strategies and random generation is designed to enhance the population’s diversity. Second, the discrete population update mechanism is introduced to balance exploration and exploitation. To further improve the solution’s quality, different local search strategies are proposed, and a Q-learning operator is introduced to choose the strategy adaptively. To further reduce the total energy consumption, an energy-saving strategy is considered. Furthermore, the Friedman test is performed to determine whether there is a significant difference between QGWO and the existing well-performing approaches at the 5% significance level. To validate the performance of QGWO, 300 instances are selected for experiments. For the overall non-dominated vector generation and inverted generational distance metrics, QGWO is ranked 1 among all compared methods. The results confirm the good performance of the proposed QGWO.

A new fractional order grey prediction model is proposed for accurate forecasting of tourism development in China. The model combines generalized fractal-fractional derivative operators with difference and accumulation generation operators. Experimental comparisons with existing models show significant improvements in accuracy and efficiency. The model is applied to forecast tourism development in China and results are compared with actual data to verify effectiveness. The proposed model combines fractal-fractional operators to improve prediction accuracy and efficiency, accounting for various factors affecting tourism development. Comparisons with existing models show superiority in accuracy and efficiency. The model accurately predicts tourism development in China, resulting in improved forecasting compared to existing methods. Comparison with actual data further validates the model by displaying agreement between predicted and actual values. Overall, the proposed model effectively captures tourism development dynamics in China for accurate forecasting.

Grey wolf optimizer (GWO) is one of the most popular metaheuristics, and it has been presented as highly competitive with other comparison methods. However, the basic GWO needs some improvement, such as premature convergence and imbalance between exploitation and exploration. To address these weaknesses, this paper develops a hybrid grey wolf optimizer (HGWO), which combines the Halton sequence, dimension learning-based, crisscross strategy, and Cauchy mutation strategy. Firstly, the Halton sequence is used to enlarge the search scope and improve the diversity of the solutions. Then, the dimension learning-based is used for position update to balance exploitation and exploration. Furthermore, the crisscross strategy is introduced to enhance convergence precision. Finally, the Cauchy mutation strategy is adapted to avoid falling into the local optimum. The effectiveness of HGWO is demonstrated by comparing it with advanced algorithms on the 15 benchmark functions in different dimensions. The results illustrate that HGWO outperforms other advanced algorithms. Moreover, HGWO is used to solve eight real-world engineering problems, and the results demonstrate that HGWO is superior to different advanced algorithms.

The permutation flow shop scheduling problem, as a classical problem in the scheduling field, is an NP‐hard problem. However, most of the reported algorithms are difficult to achieve good accuracy and efficiency. To address this problem, a hybrid grey wolf optimizer (HGWO) is proposed in this paper. First, one cooperative initialization strategy is proposed to improve the quality of the initial solution based on the improved Nawaz‐Enscore‐Ham (NEH) method and the tent chaotic map method. Second, a levy flight strategy is introduced to balance the exploitation and exploration of the algorithm for the problem's characteristics. Third, the crossover and mutation strategy, and the critical block exchange based on critical path strategy are proposed to avoid falling into the local optimum. In addition, for the best individual, the variable neighborhood descent strategy is proposed to enhance the convergence accuracy of the algorithm. To verify the performance of the proposed algorithm, three different types of instances are selected for comparison experiments with other existing methods, and the experimental results show that the proposed HGWO outperforms other comparison algorithms in solving the problem.

Sand cat swarm optimization (SCSO) is a recently introduced popular swarm intelligence metaheuristic algorithm, which has two significant limitations – low convergence accuracy and the tendency to get stuck in local optima. To alleviate these issues, this paper proposes an improved SCSO based on the arithmetic optimization algorithm (AOA), the refracted opposition-based learning and crisscross strategy, called the sand cat arithmetic optimization algorithm (SC-AOA), which introduced AOA to balance the exploration and exploitation and reduce the possibility of falling into the local optimum, used crisscross strategy to enhance convergence accuracy. The effectiveness of SC-AOA is benchmarked on 10 benchmark functions, CEC 2014, CEC 2017, CEC 2022, and eight engineering problems. The results show that the SC-AOA has a competitive performance.

This study aims to explore the influence of organizational learning and dynamic capability on organizational performance of human resource service enterprises with the moderating role of technology environment and market environment. Data were gathered from 360 human resource service enterprises, and applied the hierarchical linear regression method and structural equation model to test the hypotheses. We found that organizational learning has a significantly positive impact on resource integration capability, as well as has a significantly positive impact on resource reconfiguration capability of human resource service enterprises. Resource integration capability and resource reconfiguration capability have a significantly positive impact on organizational performance. Moreover, results indicated that the resource integration capability and resource reconfiguration capability partially mediate in the relationship between organizational learning and organizational performance. Furthermore, technology environment and market environment have positive moderation effect between resource integration capability and organizational performance of human resource service enterprises, as well as have positive moderation effect between resource reconfiguration capability and organizational performance of human resource service enterprises. The current study contributes to a better understand the impact mechanism of organizational learning on organizational performance from the perspective of organizational learning theory and dynamic capability theory. In addition, this study provides implications for human resource service enterprises and managers to improve organizational performance.