Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R²).

The two-sided disassembly line is popular for its high-efficiency disassembly of large-volume end-of-life products. However, in the process of two-sided disassembly, some parts and components need to be disassembled in parallel, and the uncertainty of disassembly time lacks certain research. This paper constructs a fuzzy multiobjective two-sided disassembly line balance problem model based on parallel operation constraint, which aims to reduce the balance loss rate, smoothness index, and energy consumption of disassembly activities. A multiobjective flatworm algorithm based on the Pareto-dominance relationship is developed. To increase the diversity of feasible solutions in the evolution process and accelerate the convergence of Pareto-optimal solutions to prevent the random search of solution space, growth, splitting and regeneration mechanisms are embedded in the algorithm. The working mechanism and efficiency of the multiobjective flatworm algorithm are proved on a series of two-sided disassembly cases, and the excellent performance of the proposed model and algorithm are demonstrated by an actual automobile two-sided disassembly line.

For the coordinated on-machine measurement of curved surfaces, the accurate prediction of the number of measuring points (MPs) could provide an important theoretical time basis for machining-inspection planning, workshop production scheduling, etc. To address the problem that the prediction of the required number of MPs for curved surfaces in relation to their complexity, machining accuracy, and other ambiguous characteristics, an adaptive neuro-fuzzy inference system (ANFIS) model optimized by the improved particle swarm optimization (IPSO) algorithm is proposed. Firstly, the input and output variables of the prediction model are defined as the surface measurement descriptor (SMD) by analyzing the impact indicators when measuring complex surfaces. Then, the IPSO algorithm with adaptive weights and particle acceleration updates is applied to optimize the ANFIS model with an organic composition of fuzzy logic knowledge and neuronal network. Finally, the prediction ability of the back propagation (BP) model, ANFIS model, and several ANFIS models optimized by the grasshopper optimization algorithm (GOA), genetic algorithm (GA), differential evolution (DE), support vector machine (SVM) and particle swarm optimization (PSO) was evaluated based on 77 groups of sample data collected from the machining-inspection experiment. The empirical results indicate that the proposed IPSO-ANFIS model with an optimal root mean square error of 2.708 and determination coefficient of 0.9855 has a higher prediction ability than GOA-ANFIS, PSO-ANFIS, DE-ANFIS, GA-ANFIS, SVM-ANFIS, ANFIS and BP models, and appears to be an effective and reliable tool for estimating the number of MPs on complex curved surfaces.