The time-dependent green vehicle routing problem with time windows is a further deepening of the research on vehicle routing problems with time windows. Its simultaneous consideration of vehicle transportation time, carbon emissions, and customer satisfaction under time-dependent variables makes it more challenging to solve than traditional vehicle routing problems. This work proposes a multi-objective optimization algorithm that combines the learnable crossover strategy and the adaptive search strategy based on reinforcement learning to overcome the local optima, poor convergence, and reduced variety of solutions that plague the multi-objective optimization algorithms when solving this problem. The proposed approach solves the problem in two stages: In the first stage, a hybrid initialization strategy is used to generate initial solutions with high quality and diversity, and crossover strategies are used to further explore the solution space and improve convergence by learning the characteristics of pareto solutions. In the second stage, the adaptive search is designed and used for learning and searching in the later stage of the algorithm. The experimental results show better solution quality obtained by the proposed approach, and the effectiveness and superiority of the proposed approach over existing methods in terms of solution convergence and diversity are demonstrated through experimental comparisons.

The study of the flexible job shop scheduling problem (FJSP) is of great importance in the context of green manufacturing. In this paper, with the optimization objectives of minimizing the maximum completion time and the total machine energy consumption, an improved multi-objective evolutionary algorithm with decomposition (MOEA/D) based on reinforcement learning is proposed. Firstly, three initialization strategies are used to generate the initial population in a certain ratio, and four variable neighborhood search strategies are combined to increase the local search capability of the algorithm. Second, a parameter adaptation strategy based on Q-learning is proposed to guide the population to select the optimal parameters to increase diversity. Finally, the performance of the proposed algorithm is analyzed and evaluated by comparing Q-MOEA/D with IMOEA/D and NSGA-II through different sizes of Kacem and BRdata benchmark cases and production examples of automotive engine cooling system manufacturing. The results show that the Q-MOEA/D algorithm outperforms the other two algorithms in solving the energy-efficient scheduling problem for flexible job shops.

The high-quality development of the manufacturing industry is not only a requirement of industrial transformation and upgrading but also an internal task for the sustainable development of manufacturing enterprises. The high-quality development of the manufacturing industry is a process of multi-participation and dynamic evolution. To understand the evolution of the high-quality development of the manufacturing industry, an evolutionary game model composed of local governments, leading enterprises, and following enterprises is constructed. The evolution process and the influence of internal factors such as innovation capability, organization construction and industry resources on the evolution and stability strategy are analysed. MATLAB software is used to perform numerical simulations. The results show that (1) there are two possible evolutionary stability strategy (ESS) models in the game, namely, {active supervision, positive participation, positive participation} and {active supervision, negative participation, negative participation}; (2) when the initial probabilities of leading and following enterprises are all at low levels, increasing any one of the local government's initial probability, innovation capability, organization construction and industry resources can promote tripartite evolution towards {active supervision, positive participation, positive participation}; and (3) the convergence rate of leading enterprises and following enterprises evolving towards a positive participation strategy will increase slightly with an increase in local government's initial probability, innovation capability, organization construction, and industry resources when both are at middle initial probability levels. The final policy suggestions can be used to help all parties choose the optimal strategy and to achieve the high-quality development target of the manufacturing industry as soon as possible.

The research on high-quality development of the manufacturing industry (HQDMI) in the Yellow River Basin is in the exploratory stage, and little literature has systematically analyzed its influencing factors. Based on the grounded theory, this paper makes an exploratory analysis of the influencing factors of the HQDMI in the Yellow River Basin. This study finds that the influencing factors of the HQDMI mainly include six aspects: innovation capability, organization construction, industry resources, institutional environment, industry collaboration, and market demand. By using the HQDMI’s endogenous and exogenous factors as the core, the realization mode of the HQDMI in the Yellow River basin is put forward, and the influencing factors and action model of the HQDMI in the Yellow River Basin are constructed. The findings have significant reference value for promoting the HQDMI in the Yellow River Basin and are helpful for improving the HQDMI research.

Scientific and accurate evaluations of the high-quality development level of the manufacturing industry have important theoretical significance and reference value for the government and for decision-making departments for the formulation of corresponding incentive measures. Firstly, based on rough set theory, this paper proposes an attribute reduction method, which can help to delete redundant indexes and reduce the calculation workload. Secondly, a more scientific combination weighting method is proposed, and the calculation method of the total index in an evaluation index system is given. Finally, the HQDMI evaluation index system is constructed based on the connotations of the high-quality development of the manufacturing industry. Taking China as an example, the total index and sub-index of high-quality development of 30 provinces in China are calculated, the high-quality development level of the manufacturing industry in 30 provinces is clustered based on SPSS24.0, and the visualization of the clustering results is achieved by ArcGIS software. The results show that the high-quality development level of China’s manufacturing industry has regional distribution characteristics. Regions with high development levels are mainly distributed in eastern coastal areas, followed by the central development level, and those of the northeast and west are low. This study provides a theoretical application mode for the evaluation of the high-quality development level of the manufacturing industry, and it has theoretical guidance significance for promoting the high-quality development of the manufacturing industry.

The vehicle routing problem (VRP) is a well-researched problem in the operations re- search literature. This paper studies a vehicle routing problem with time windows. Based on Solomon’s research, an improved sequential initialization algorithm, Solomon i1 with DTWC (Solomon i1 with Dynamic Time Windows Compatibility), is proposed in this paper to construct feasible initial solutions with higher quality in less calculation time. A tabu search with VNS (Variable Neighborhood Search) and the Metropolis criterion is used to further optimize the solutions that the proposed initialization algorithm provides. We run computational experiments on cases of well-known problems. Results show that compared to the initialization method before the change, our improved initialization approach performs well in terms of providing a better feasible initialization solution in less time, with an average 10% improvement in solution quality and an average 34% computational time savings. The proposed initialization algorithm’s superiority is also demonstrated by the tabu search’s better performance on the initial solutions it produces.