This paper proposes an evolutionary approach for optimizing crew roster recovery (CRR) problems, in which rosters for multiday flight duties are reassigned after disruptions, under a set of constraints involving practical management issues, safety regulations, and preallocated activities. In the proposed approach, CRR problems are first formulated as combinational optimization problems containing multiple objectives and constraints, and a variant of the nondominated sorting genetic algorithm II method is used to explore Pareto solutions. To analyze the effectiveness of the proposed approach, a real-world rostering problem was studied. For the test instance, experimental results showed the advantages of the proposed approach compared with previous work in the literature. By simulating disruption events on the real-world rostering plan, we studied two recovery scenarios containing sufficient and insufficient available crews in the experiments. In particular, a constraint-loosening mechanism that conditionally replaced preallocated tasks with flight duties was proposed in a resource-shortage case to explore the Pareto solutions. The experimental results show that the proposed approach attains a favorable solution quality and can generate multiple recovery plans for decision makers.

Reheating furnaces are used to homogeneously reheat the steel stock (Billets, blooms or slabs) at a temperature between 1000°C and 1250°C before hot rolling. Supply of accurate, stable, and reliable control of temperature is most important for reheating furnaces in hot-rolled steel production. The phenomenon of large time lags in temperature is an arduous problem that existed in the combustion system of furnaces, it causes control system big overshoot, continuous oscillation, and may even make the system unstable. In this paper, a prediction model based on gate recurrent unit (GRU) was established to forecast the inside temperature of the furnace by using temperature, fuel, and air time series. Moreover, this paper presents an approach which is combining a prediction model with a feedforward controller that can improve the stability of the temperature control system. Established prediction model of temperatures by collecting data from on-side, and evaluated the model and feedforward performance on the actual reheating furnace. Compared with other dynamic models (recurrent neural network and long short-term memory), the proposed models outperformed by 15.63% and 26.07% on average in terms of the mean absolute error and root mean square error, respectively. Moreover, the proposed control improve traditional PI controller by 33.43% and 19.92% on average in terms of the mean absolute error and root mean square error, respectively. The presented method can be used to reduce the temperature disturbances in the reheating furnaces.

In automobiles, lock parts are matched with inserts, and this is a crucial quality standard for the dimensional accuracy of the molding. This study employed moldflow analysis to explore the influence of various injection molding process parameters on the warpage deformation. Deformation of the plastic part is caused by the nonuniform product temperature distribution in the manufacturing process. Furthermore, improper parameter design leads to substantial warpage and deformation. The Taguchi robust design method and gray correlation analysis were used to optimize the process parameters. Multiobjective quality analysis was performed for achieving a uniform temperature distribution and reducing the warpage deformation to obtain the optimal injection molding process parameters. Subsequently, three water cooling system designs—original cooling, U-shaped cooling, and conformal cooling—were tested to modify the temperature distribution and reduce the warpage. Taguchi gray correlation analysis revealed that the main influencing parameter was the mold temperature followed by the holding pressure. Moreover, the results indicated that the conformal cooling system improved the average temperature distribution.

Nanofluid/ultrasonic atomization minimal quantity lubrication (MQL) was used in an SKD11 steel micro-milling process with ultrasonic dispersion. The nanofluid atomizer substantially improved nanofluid reunification and delivery to the cutting zone. The micro-milling cutting force and temperature were used to calculate the signal-to-noise ratio (S/N). Normalization of the S/N in a grey relational analysis obtained a grey correlation coefficient, which was used in a fuzzy analysis to obtain a multiple performance characteristic indexes (MPCI). The MPCI was maximized by optimizing the process parameters for multiple quality characteristics and then comparing the optimized parameters with those in the MQL system. Experimental results indicate that the nanofluid parameters containing the density of nanofluid, feed rate, and distance of nozzle are the most influential control factors, while 3.617 Newton and 117.1 degrees Celsius, respectively, are the optimal combination of parameters. Different lubricating methods were also compared in terms of effects on micro-milling cutting force, micro-milling temperature, micro-milling tool wear, and surface burr. Experiments showed that nanofluid (MWCNTs)/ultrasonic atomization MQL obtained the best results.

The introduction of a multi-population structure in differential evolution (DE) algorithm has been proven to be an effective way to achieve algorithm adaptation and multi-strategy integration. However, in existing studies, the mutation strategy selection of each subpopulation during execution is fixed, resulting in poor self-adaptation of subpopulations. To solve this problem, a dynamic multi-strategy integrated differential evolution algorithm based on reinforcement learning (RLDMDE) is proposed in this paper. By employing reinforcement learning, each subpopulation can adaptively select the mutation strategy according to the current environmental state (population diversity). Based on the population state, this paper proposes an individual dynamic migration strategy to “reward” or “punish” the population to avoid wasting individual computing resources. Furthermore, this paper applies two methods of good point set and random opposition-based learning (ROBL) in the population initialization stage to improve the quality of the initial solutions. Finally, to evaluate the performance of the RLDMDE algorithm, this paper selects two benchmark function sets, CEC2013 and CEC2017, and six engineering design problems for testing. The results demonstrate that the RLDMDE algorithm has good performance and strong competitiveness in solving optimization problems.