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 effect of linear non-isothermal aging and composite non-isothermal aging on the mechanical properties and corrosion resistance of 2A12 aluminum alloy was investigated. Optical microscopy (OM) and scanning electron microscopy (SEM) equipped with energy-dispersive spectroscopy (EDS) were used to study the microstructure and intergranular corrosion morphology, and the precipitates were analyzed using X-ray diffraction (XRD) and transmission electron microscopy (TEM). The results showed that the mechanical properties of 2A12 aluminum alloy were improved by both non-isothermal aging techniques due to the formation of an S′ phase and a point S″ phase in the alloy matrix. Linear non-isothermal aging resulted in better mechanical properties than composite non-isothermal aging. However, the corrosion resistance of the 2A12 aluminum alloy was reduced after non-isothermal aging due to the transformation of matrix precipitates and grain boundary precipitates. The corrosion resistance of the samples followed the order: annealed state > linear non-isothermal aging > composite non-isothermal aging.

At present, the demand for ready-mixed concrete (RMC) in construction industry is increasing day by day, and the supply mode of multiple delivery depots corresponding to multiple construction sites has been widely used. In order to further improve the joint distribution efficiency between various delivery depots, this research establishes a multiobjective optimal distribution model with time window constraints and demand postponement attributes for the problem that the subbatching plants need to work together. The model divides the reasons for demand postponement into two types: the constraint for timely unloading of trucks cannot be met on time and the constraint for timely pouring at the construction site cannot be met on time. This work improved the coding method of genetic algorithm based on the characteristics of the distribution model. Using hierarchical real-coding form, the coding operator of each layer can be evolved separately, which ensures the globality of the search, and, at the same time, an improved immune operator is added to ensure the local search performance. By comparison, the results obtained by improved GA are 7.05% higher than those of the standard GA, and the early convergence speed of improved GA is obviously better than that of the standard GA. The simulation experiments show that the total trucks’ waiting time during the process of providing delivery services from 5 concrete plants to 8 construction sites is 769 minutes, and the total waiting time of 8 construction sites is 507 minutes. Through practical case analysis, this work can enable RMC production enterprises and construction sites to effectively reduce the waiting time of corresponding operations, and the obtained results are close to the simulation results. The proposed method indeed improves the efficiency of RMC distribution.

During the sand-making process, the wear of the split cone will affect the dynamic balance of the rotor and the sand-making effect of the crusher. This paper focuses on experiments and simulations with the rotor of a vertical shaft impact crusher. A wear model for predicting the wear value of the split cone is established and its effectiveness is verified. The orthogonal test of is designed. By using the range analysis method, the influence of the rotor speed, the feed height, the installation angle, and the number of the guide plate on the wear resistance of the split cone are studied. By using the multiple linear regression analysis method, the optimal parameter combination of each factor is obtained. The results of this paper show that the wear resistance of the split cone is the best when the installation angle of the guide plate is 40°, the installation number of the guide plate is 3, the rotor speed is 2800 r/min, and the feed height is 40 mm.

In this paper, for the problem of high total fuel consumption of distribution trucks when multiple concrete-mixing plants distribute concrete together, we established a green fuel consumption model for distribution trucks and solved the model with an improved genetic algorithm to obtain a green distribution scheme for trucks. Firstly, the fuel consumption model is established for the characteristics of commercial concrete tankers; secondly, the adaptive elite retention strategy, adaptive crossover, mutation operator, and immune operation are added to the genetic algorithm to improve it; and finally, the model is solved to obtain the green distribution scheme. The total fuel consumption in this experiment was 189.6 L when the green distribution scheme was used; compared to the total fuel consumption under the original scheme (240 L), the total fuel consumption was reduced by 21.25%. The experimental results show that the total fuel consumption of delivery trucks can be significantly reduced based on the established green fuel consumption model, and the improved genetic algorithm can effectively solve the model.

Cold chain logistics distribution, a vital activity supporting global urbanization, faces complex challenges in balancing economic, environmental, and social objectives. This study investigates the green vehicle routing problem with a mixed fleet, simultaneously optimizing total cost, carbon emissions, and customer satisfaction. To solve this NP-hard problem, a novel multi-strategy NSGA-III algorithm is proposed, which integrates an adaptive pheromone update mechanism, elite route guidance, and genetic operators to significantly enhance search efficiency and solution diversity in complex solution spaces. Computational experiments on benchmark instances and a real-world case demonstrate the algorithm’s superior performance over mainstream multi-objective optimizers like NSGA-III and NSGA-II in metrics such as hypervolume. Sensitivity analysis further elucidates the impact of key operational parameters on system performance and provides a quantitative decision-making basis for greening urban cold chain fleets. This research offers an effective computational tool for complex sustainable logistics problems, with a modeling framework extensible to other industrial systems facing similar multi-objective trade-offs.