A Production Planning Model for Make-to-Order Foundry Flow Shop with Capacity Constraint

The mode of production in the modern manufacturing enterprise mainly prefers to MTO (Make-to-Order); how to reasonably arrange the production plan has become a very common and urgent problem for enterprises’ managers to improve inner production reformation in the competitive market environment. In this paper, a mathematical model of production planning is proposed to maximize the profit with capacity constraint. Four kinds of cost factors (material cost, process cost, delay cost, and facility occupy cost) are considered in the proposed model. Different factors not only result in different profit but also result in different satisfaction degrees of customers. Particularly, the delay cost and facility occupy cost cannot reach the minimum at the same time; the two objectives are interactional. This paper presents a mathematical model based on the actual production process of a foundry flow shop. An improved genetic algorithm (IGA) is proposed to solve the biobjective problem of the model. Also, the gene encoding and decoding, the definition of fitness function, and genetic operators have been illustrated. In addition, the proposed algorithm is used to solve the production planning problem of a foundry flow shop in a casting enterprise. And comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm

Integrated scheduling of distributed production and distribution in group manufacturing with uncertain travel time

Abstract This paper presents a novel integrated distributed production and distribution scheduling problem in group manufacturing with uncertain travel time (IDPDSP-GM-UTT), in which products are firstly produced in several distributed hybrid flow shops and then delivered to several retailers in batches. The proposed model considers both geographical dispersion of multi-factories and variable travel time between factories and retailers caused by time-varying dynamics of road network, which describes the production environment more authentic. Additionally, a mathematical model is developed to find the optimal quantity of raw material, delivery plan, and punishment of earliness and tardiness with the objective of minimizing total costs. Then, an improved genetic algorithm with two-stage heuristic mutation scheduling strategy and tabu search for local optimization (GA-2HMS&TS) is designed to solve the proposed model. To verify the performances of the proposed method, several experiments by adopting test experimental examples with different scales are performed. The computational results exhibit that the GA-2HMS&TS not only significantly reduces the total cost of production and distribution, but also outperforms all of its rivals. In addition, the robustness of the proposed models is also analyzed with regard to the different road conditions

Segmentation Head Networks with Harnessing Self-Attention and Transformer for Insulator Surface Defect Detection

Current methodologies for insulator defect detection are hindered by limitations in real-world applicability, spatial constraints, high computational demand, and segmentation challenges. Addressing these shortcomings, this paper presents a robust fast detection algorithm combined segmentation head networks with harnessing self-attention and transformer (HST-Net), which is based on the You Only Look Once (YOLO) v5 to recognize and assess the extent and types of damage on the insulator surface. Firstly, the original backbone network is replaced by the transformer cross-stage partial (Transformer-CSP) networks to enrich the network’s ability by capturing information across different depths of network feature maps. Secondly, an insulator defect segmentation head network is presented to handle the segmentation of defect areas such as insulator losses and flashovers. It facilitates instance-level mask prediction for each insulator object, significantly reducing the influence of intricate backgrounds. Finally, comparative experiment results show that the positioning accuracy and defect segmentation accuracy of the proposed both surpass that of other popular models. It can be concluded that the proposed model not only satisfies the requirements for balance between accuracy and speed in power facility inspection, but also provides fresh perspectives for research in other defect detection domains

A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network

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 Fuzzy DEA-Based Manufacturing Service Efficiency Evaluation and Ranking Approach for a Parallel Two-Stage Structure of a Complex Product System on the Example of Solid Waste Recycling

Accurate production efficiency evaluation can assist enterprises in adjusting production strategies, improving production efficiency, and, thereby, weakening environmental impacts. However, the current studies on production efficiency evaluation do not accurately consider interactions inside the production system in parallel production processes. Based on the concept of the manufacturing service, this paper describes the production process of a complex product system (CoPS) with a manufacturing service chain. An efficiency calculation model based on the triangular intuitionistic fuzzy number–solid waste recycling–super-efficiency data envelopment analysis (TIFN-SWR-SDEA) is proposed under the consideration of the internal parallel structure of the production system on the example of solid waste recycling. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) method and the entropy weight method were combined to determine the proportion of solid waste recycling, and an improved proposed index rank (PIR) method was employed to rank the efficiency interval results. Finally, the effectiveness and superiority of the method were verified by comparative analysis. The results show that the overall efficiency of the CoPS production system can be improved by using green manufacturing technology, increasing the recycling of renewable resources, using clean energy, and improving the utilization rate of materials in the production process