Improve Production Efficiency Research Articles

СОВРЕМЕННЫЕ ВЫЗОВЫ ЦИФРОВОЙ ТРАНСФОРМАЦИИ СТРОИТЕЛЬНОЙ СФЕРЫ

The impact of digitalization and digital transformation of the construction sector on the country's economy can hardly be overestimated, as it stimulates economic growth, improves production efficiency, increases labor productivity and reduces costs, and enables market players to form a competitive advantage. In 2024, the Russian construction sector is transitioning to the mandatory use of information modeling technologies, which indicates the progressive integration of digital technologies in construction. The purpose of this article is to identify the challenges and trends of digital transformation of the construction industry in 2024, formed by the conditions of the current turbulent economic situation in Russia. The article analyzes domestic and foreign scientific publications in the field of digitalization of construction, as well as current statistical and analytical data.

Critical Information Mining Network: Identifying Crop Diseases in Noisy Environments

When agricultural experts explore the use of artificial intelligence technology to identify and detect crop diseases, they mainly focus on the research of a stable environment, but ignore the problem of noise in the process of image acquisition in real situations. To solve this problem, we propose an innovative solution called the Critical Information Mining Network (CIMNet). Compared with traditional models, CIMNet has higher recognition accuracy and wider application scenarios. The network has a good effect on crop disease recognition under noisy environments, and can effectively deal with the interference of noise to the recognition effect in actual farmland scenes. Consider that the shape of the leaves can be symmetrical or asymmetrical.First, we introduce the Non-Local Attention Module (Non-Local), which uses a unique self-attention mechanism to fully capture the context information of the image. The module overcomes the limitation of traditional convolutional neural networks that only rely on local features and ignore global features. Global features are particularly important when the image is disturbed by noise. Non-Local improves a more comprehensive visual understanding of crop disease recognition. Secondly, we have innovatively designed a Multi-scale Critical Information Fusion Module (MSCM). The module uses the Key Information Extraction Module (KIB) to dig into the shallow key features in the network deeply. The shallow key features strengthen the feature perception of the model to the noise image through texture and contour information, and then the shallow key features and deep features are fused to enrich the original deep feature information of the network. Finally, we conducted experiments on two public datasets, and the results showed that the accuracy of our model in crop disease identification under a noisy environment was significantly improved. At the same time, our model also showed excellent performance under stable conditions. The results of this study provide favorable support for the improvement of crop production efficiency.

The Impact of Agricultural Socialized Service on Grain Production: Evidence from Rural China

Although China’s grain production has reached nineteen consecutive harvests, the uncertainty of the current domestic and international environment has put more pressure on further increasing grain production in the future. For the past few years, agricultural socialization services have been crucial in boosting grain production and farmers’ revenue by addressing the issue of land cultivation and farming methods. In this regard, the question of whether and how agricultural socialized services may resolve the present grain production conundrum is extremely practical. Therefore, the study employs the China Rural Revitalization Survey data of 3709 households. Based on the 2SLS model, stepwise regression method, and moderated effects model, it creatively takes into account a variety of agricultural production segments, investigates the mechanism of services on grain production from the standpoint of improved production efficiency and plot concentration, and further examines the effects of aging populations and regional variations in grain production areas. The study found the following: (1) The average proportion of grain production area of farmers in the sample is 49%, and 42% of farmers have purchased agricultural socialization services. (2) Agricultural socialization services can significantly promote farmers’ grain cultivation behavior by facilitating connected transfers in and inhibiting connected transfers out to take advantage of plot concentration, and boosting the use of agricultural machines to enhance output efficiency. (3) The aging of the agricultural population will, to a certain extent, strengthen the promoting effect of agricultural socialization services on grain cultivation. Agricultural socialization services affect grain cultivation more in main grain-producing areas. Therefore, emphasizing the role of agricultural socialization services in accelerating the shift to moderate-scale operations, decreasing the non-grain component of the planting structure, and promoting the implementation of policies tailored to actual production needs are important steps to safeguard the production capacity of grain in different regions.

Homogenization Path Based on 250 mm × 280 mm Bloom under Mixed Light and Heavy Presses: Simulation and Industrial Studies

This study proposed a new method for homogenizing continuous casting blooms based on solidification simulation calculations and industrial tests. The text describes a theoretical analysis of the solidification route of a cast billet of high-carbon alloy steel (B300A) under different process conditions. It summarizes the changing law of different under-pressure process parameters and under-pressure efficiency. The text also presents a solution to the seriousness of center shrinkage defects in the continuous casting of a large square billet of high-carbon alloy steel with the synergistic control technology of mixed light and heavy mixing under pressure. The study indicates that the center carbon segregation index of a high carbon steel continuous casting billet is 1.05, with a carbon extreme difference of not more than 0.08% and a proportion of 98.4%. Additionally, the center shrinkage is not more than a 0.5 level with a proportion of 99.5%. Meanwhile, the internal quality of cast billets has been improved, allowing for the rolling of large-size bars with a low consolidation ratio. The pass rate for internal ultrasonic flaw detection using the GB/T4162A grade is now higher than 99.95%, significantly reducing process costs and improving production efficiency for continuous casting and rolling.

Facile and efficient fabrication of carbon nanotube reinforced epoxy vitrimers through dynamic crosslinking

AbstractCarbon nanotubes (CNT) is an ideal candidate for reinforced epoxy vitrimer composites, however, achieving a desirable improvement of the mechanical properties is a great challenge owing to CNT is extremely easy to aggregate. In this study, well‐dispersed CNT reinforced epoxy vitrimers, namely EVD/CNT, were prepared by combining ultrasonic dispersion and dynamic crosslinking techniques, using multiple wall carbon nanotube as reinforcement filler, sebacic acid as curing agent, diglycidyl ether of bisphenol A (DGEBA) as epoxy monomer, zinc acetylacetonate as transesterification catalyst, torque rheometer (internal mixer) as dynamic crosslinking equipment. The CNT reinforced epoxy vitrimers, namely EVS/CNT, were prepared using traditional static curing method. The structure and morphology, mechanical properties and stress relaxation behavior of EVS/CNT and EVD/CNT were comparatively investigated, and the influence of CNT content on the structure and properties of EVD/CNT prepared by dynamic crosslinking method were also studied. The results indicated that dynamic crosslinking technology can stably disperse CNT into epoxy vitrimer resin matrix under continuous shear force. However, CNT experienced severe secondary agglomeration during the static curing process. When the loading amount of CNT was 2 wt%, the tensile strength and elongation at break of EVD/CNT‐2 were 3.2 times and 2.2 times higher than those of EVS/CNT‐2, respectively. The Young's modulus and glass transition temperature (Tg) of EVD/CNT‐2 (6.43 ± 1.10 MPa, 29.6°C) were also higher than those of EVS/CNT‐2 (5.46 ± 0.31 MPa, 25.4°C). Moreover, dynamic crosslinking technology greatly shortened the reaction time of CNT reinforced epoxy vitrimers, improved production efficiency and reduced reaction energy consumption, and was expected to be used for large‐scale and industrial production.

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Characterizing Spatial and Temporal Variations in N2O Emissions from Dairy Manure Management in China Based on IPCC Methodology

The emission factor method (Tier 1) recommended by the Intergovernmental Panel on Climate Change (IPCC) is commonly used to estimate greenhouse gas (GHG) emissions from livestock and poultry farms. However, the estimation accuracy may vary due to practical differences in manure management across China. The objectives of this study were to estimate the direct and indirect nitrous oxide (N2O) emissions from dairy manure management between 1990 and 2021 in China and characterize its spatial and temporal variations following IPCC guideline Tier 2. The N2O emission factor (EF) of dairy cow manure management systems was determined at the national level and regional level as well. The results showed that the national cumulative N2O emission of manure management from 1990 to 2021 was 113.1million tons of CO2 equivalent, ranging from 90.3 to 135.9 million tons with an uncertainty of ±20.2%. The annual EF was 0.021 kg N2O-N (kg N)−1 for total emissions, while it was 0.014 kg N2O-N (kg N)−1 for direct emissions. The proportions of N2O emissions in North China, Northeast China, East China, Central and Southern China, Southwest China and Northwest China were 32.3%, 18.6%, 11.4%, 5.8%, 6.1% and 25.8%, respectively. In addition, N2O emissions varied among farms in different scales. The respective proportions of total N2O emissions from small-scale and large-scale farms were 64.8% and 35.2% in the past three decades. With the improvement in farm management and milk production efficiency, the N2O emissions per unit mass of milk decreased from 0.77 × 10−3 kg to 0.48 × 10−3 kg in 1990–2021. This study may provide important insights into compiling a GHG emission inventory and developing GHG emission reduction strategies for the dairy farming system in China.

Validation of genomic tools to improve production efficiency, health resilience and the carbon footprint of beef production

Genomic tools and accuracy of molecular breeding values (MBVs) for economically important traits continue to improve and are being applied in commercial crossbred beef cattle. Ge­nomic breed composition and retained heterozygosity of an individual animal can be used to further improve female fertil­ity, stayability, lifetime productivity and calf health resilience. But this is just the first step. Within specific crossbred popula­tions, researchers at Livestock Gentec have used large numbers of cattle phenotypes and genotypes, combined with advanced statistical genomic analyses to generate MBVs for 18 traits with moderate to moderately-high accuracy (0.35 to 0.60). This accu­racy is equivalent to 10 to 20 progeny records. These MBVs are economically balanced into multi-trait profit indices such as the Replacement Heifer Profit Index Score and the Feeder Profit In­dex. The use of these genomic tools will improve the sustainabil­ity of beef production by improving profitability, lowering the carbon footprint, and improving an animal’s ability to adapt to changing environments. The following paper outlines the valida­tion of these genomic tools and their potential benefits.

Comparative Study on Flow Characteristics in Deep Marine and Marine-Continental Transitional Shale in the Southeastern Sichuan Basin, China.

Permeability is a significant characteristic of porous media and a crucial parameter for shale gas development. This study focuses on deep marine and marine-continental transitional shale in the southeastern Sichuan area using the gas pulse decay testing method to systematically analyze the gas permeability, stress sensitivity, and gas transport mechanisms of shale under different pressure conditions and directions. The results show that the porosity and gas permeability of the deep marine shale are greater compared to those of the marine-continental transitional shale. The elevated fluid pressure in the deep marine shale offers superior conditions for the preservation of nanopores, while the high quartz content provides advantageous conditions for fluid transport in nanopore channels. The permeability and stress sensitivity of the deep marine shale are greater than those of the marine-continental transitional shale, and the stress sensitivity is greater in the perpendicular bedding direction than in the parallel bedding direction, possibly related to the mineral composition of shale and the compaction it has undergone. The flow mechanism of the deep marine shale is transition flow and Knudsen flow, while that of the marine-continental transitional shale is transition flow. The deep marine shale possesses smaller nanopore sizes and a higher quantity of micropores, which create advantageous conditions for gas transport within nanopores. During the process of extracting shale gas, the extraction of gas causes a decrease in pore pressure and an increase in effective stress, resulting in a reduction in permeability. However, when the pore pressure reaches a specific value, the enhanced slippage effect leads to an increase in permeability, which is advantageous for gas extraction. In the later stage of shale gas well production, intermittent production plans can be developed considering the strength of the slippage effect, leading to a significant improvement in production efficiency.

Precision forecasting of grinding wheel Wear: A TransBiGRU model for advanced industrial predictive maintenance

In intelligent manufacturing, accurate prediction of grinding wheel wear is essential to reduce maintenance costs and improve production efficiency. To achieve accurate forecasts, this paper introduces a grinding wheel wear prediction model, TransBiGRU, incorporating a Transformer encoder, Bidirectional Gated Recurrent Unit (BiGRU), positional encoding layer, and position-wise feedforward layer. The model extracts features by analyzing current signal characteristics in basic statistical, time, and frequency domains during the machining process. Training is conducted through K-fold cross-validation to ensure model stability. The experimental results indicate that the model achieved good performance with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2) evaluation metrics, obtaining values of 2.0898, 3.262, and 0.9338, respectively. Systematically reducing modules validates the importance of each module in enhancing predictive capabilities. This research achieves the practical application of low-cost current sensors in optimizing maintenance plans and reducing downtime in predicting grinding wheel wear.

Improved genetic algorithm based on rule optimization strategy for fibre allocation

ABSTRACT In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to tackle the nondeterministic polynomial hard problem stemming from inventory fibre resources and fibre selection principles in optical cable production. The algorithm aims to maximize inventory score and minimize fibre segmentation rate. It employs a permutation encoding approach to link the genetic algorithm with fibre allocation solutions and applies a self-attention mechanism to determine subset solution weight within each solution. To boost the recombination of favourable gene segments from different chromosomes, a rule optimization strategy is integrated into the crossover operation based on the weights. This operations enhance the algorithm's global search capability and convergence speed. A feasibility repair strategy is then used to inspect and rectify chromosomes, preventing the generation of illegal solutions. The legitimate mutation operation, founded on weight optimization rules, effectively reduces the algorithm's running time by avoiding illegal solutions. By leveraging actual production data from an optical cable manufacturer for simulation, the experimental results confirm the effectiveness of the improved genetic algorithm in addressing the fibre allocation problem. Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.

A Framework on Group Data Sharing Based Agreement Implementation in Cloud Computing

Abstract -- Cloud computing and cloud storage have become hot topics in recent decades. Both are changing the way we live and greatly improving production efficiency in some areas. At present, due to limited storage resources and the requirement for convenient access, we prefer to store all types of data in cloud servers, which is also a good option for companies and organizations to avoid the overhead of deploying and maintaining equipment when data are stored locally. The cloud server provides an open and convenient storage platform for individuals and organizations, but it also introduces security problems. Due to the particularity of some industries, some enterprises have extremely high requirements on the security of data. If the data is lost or destroyed, the enterprise will bear serious consequences. The second problem is reliability. Only by ensuring that the data records are accurate and reliable can the data transmission be more secure and the enterprise win a higher reputation. In addition, the issue of cloud computing regulation is also very important. Only through efficient and orderly regulation can enterprises operate healthily and normally. At present, many small and medium sized enterprises need to spend huge costs on data storage, which will continue to increase with the expansion of data volume. However, the emergence of cloud storage services has brought the dawn for these enterprises, they will need to store the data transmission to the cloud, through the cloud storage service provider to solve this problem, so that they can save the cost of their own data center, use low price to achieve the purpose of data storage.

Safe Energy using Hydraulic System Analysis at a Hydropower Plant

The Hydro-PowerStation is one of the keys (important) sources of electricity, and it has a digital governor, which improves production efficiency over mechanical governors. This research looked at the hydraulic turbine, found flaws such as oil leakage from the pumps utilized, calculated the amount of energy consumed, and worked to reduce it by measuring the losses and monitoring the poor process during periods of high water load of up to 83%. As a consequence of the research, it was discovered that the amount of energy consumed had decreased by 20%, by employing well-made, effective pumps for each unit according to its use, oil leakage in all units is reduced.

The Effect of Virtual Reality-Enabled Manufacturing Practices on Production Efficiency

Firms have been increasingly adopting virtual reality (VR) technologies for manufacturing purposes, but it is still unclear whether and how such VR-enabled manufacturing practices (VRMPs) can help firms improve production efficiency. Analyzing a sample of 87 US treatment firms that have adopted VRMPs and 87 matched control firms without such adoption over the period of 2010–2020, our difference-in-differences estimation suggests that the treatment firms gain more production efficiency improvement when compared with the matched control firms. We also find that the production efficiency improvement arising from VRMPs is more pronounced for firms facing high levels of labor volatility and market dynamism. Our additional analysis further suggests that applying VR technologies into different manufacturing stages and in different industrial sectors can result in different degrees of production efficiency improvement. Overall, our study demonstrates the positive effect of VRMPs on production efficiency but also reveals how the effect can vary across firms depending on their operating environments, VR application fields, and industrial sectors.

Partial Cross Mapping Based on Sparse Variable Selection for Direct Fault Root Cause Diagnosis for Industrial Processes.

Root cause diagnosis of process industry is of significance to ensure safe production and improve production efficiency. Conventional contribution plot methods have challenges in root cause diagnosis due to the smearing effect. Other traditional root cause diagnosis methods, such as Granger causality (GC) and transfer entropy, have unsatisfactory performance in root cause diagnosis for complex industrial processes due to the existence of indirect causality. In this work, a regularization and partial cross mapping (PCM)-based root cause diagnosis framework is proposed for efficient direct causality inference and fault propagation path tracing. First, generalized Lasso-based variable selection is performed. The Hotelling T2 statistic is formulated and the Lasso-based fault reconstruction is applied to select candidate root cause variables. Second, the root cause is diagnosed through the PCM and the propagation path is drawn out according to the diagnosis result. The proposed framework is studied in four cases to verify its rationality and effectiveness, including a numerical example, the Tennessee Eastman benchmark process, the wastewater treatment process (WWTP), and the decarburization process of high-speed wire rod spring steel.

Evaluating edge artificial intelligence-driven supply chain management platforms using collaborative large-scale fuzzy information fusion

The rapid development of edge artificial intelligence (AI) has brought about revolutionary changes in supply chain management (SCM). It not only provides real-time data processing capabilities but also accelerates the decision-making process, injecting more innovative elements into SCM. In this context, SCM platforms require new technological developments and effective evaluations, as they collectively drive the efficient coordination of complex business processes. SCM platforms enable the seamless coordination of various supply chain elements, facilitating streamlined operations and enhanced decision-making processes. The evaluation of these platforms not only serves to validate their performance and effectiveness, but also contributes to continual improvements in design and application, and addressing the evolving demands within the realms of global commerce and dynamic market conditions. The ongoing assessment and enhancement of SCM platforms play a crucial role in optimizing resource allocation, improving production efficiency, and fostering adaptability to changing market dynamics. However, the acquisition of assessment data often introduces imprecise data. Additionally, the large-scale nature and bounded rationality of decision-makers (DMs) significantly impact the evaluation of SCM platforms. This article aims to explore a collaborative large-scale information fusion approach and provide a robust fuzzy framework for evaluating SCM platforms. This approach employs a combination of spherical fuzzy sets (SFSs), large-scale group decision-making (LSGDM), behavioral theories and three-way decisions (TWD) to thoroughly explore collaborative large-scale information fusion and its practical application in assessing SCM platforms. It introduces an innovative spherical fuzzy (SF) LSGDM technique and integrates TWD with the inclusion of prospect theory (PT) and regret theory (RT) to mitigate potential decision risks. The developed collaborative large-scale information fusion approach is assessed for validity, effectiveness and practicality in the context of evaluating SCM platforms using online data. Experimental results demonstrate that this approach provides reasonable evaluation outcomes, considering uncertain information processing capabilities, large-scale characteristics, bounded rationality and decision risks.

Multi-agent reinforcement learning method for cutting parameters optimization based on simulation and experiment dual drive environment

Improving production efficiency while ensuring product surface quality is a constant focus of manufacturers. Cutting parameter optimization is an important technique for ensuring high-efficiency and high-quality production. In this paper, a novel method for cutting parameter optimization that integrates multi-agent reinforcement learning with a dual-drive virtual machining environment is proposed. First, a feature extraction, fusion and generation model for cutting simulation and experimental data is proposed to solve the problem of incomplete data acquisition in the production process. Second, a Markov decision model for optimizing cutting parameters is defined, and a virtual machining environment driven by both simulation and experimental data is constructed. Third, a novel multi-agent reinforcement learning method called Q-MIX-MATD3, in which a twin delay deep deterministic policy gradient, value function decomposition and a teacher model are combined, is proposed to explore the cutting parameter optimization policy by interacting with the virtual machining environment. Finally, the proposed method is verified on a commutator production line. Moreover, the results show that the accuracy of the virtual machining environment driven by both simulation and experiment increases by more than 5 %, response efficiency increases by 31 %, and Q-MIX-MATD3-based cutting parameter optimization method reduces time cost by 98 % and achieves the optimization effect of the classical optimization method.

A bearing fault diagnosis method based on M-SSCNN and M-LR attention mechanism

Bearing fault diagnosis is vital for mechanical maintenance and fault prediction. It ensures equipment safety, extends lifespan, reduces maintenance costs, and improves production efficiency. Nevertheless, it should be acknowledged that some existing diagnostic methods have achieved high accuracy rates in certain scenarios. However, the challenge lies in their limited generalization capabilities, which can lead to reduced accuracy when applied to diverse or unseen conditions. In this study, we proposed a new bearing fault diagnosis method to address the issue of low accuracy caused by the inadequate generalization of models in the process of rolling bearing fault diagnosis. The method is based on a multi-scale sliding convolution neural network and multi-level residual attention mechanism, the model exhibits high accuracy, strong generalization capability, and lightweight structure. Firstly, the time domain signal of the bearing vibration is converted into a two-dimensional time–frequency map, and the image is pixel-segmented using superpixel segmentation techniques. Next, a multi-scale parallel convolution approach is used to extract features to improve the adaptability and robustness of the model to objects of different sizes and scales. Sliding convolution is used instead of pooling to avoid the problem of feature loss caused by maximum pooling and average pooling. A multi-level attention mechanism is then introduced for all stacked channels to focus on the more important and critical information of the module, and residual connections are added to prevent degradation of the network performance. Finally, the proposed method is passed through the fully connected layer for classification using the Softmax classifier. Experimental verification using public datasets and experimental data of our research group shows that the proposed method has better performance than the existing diagnostic methods and diagnostic models. The proposed method offers an advanced and innovative solution in the domain of bearing fault diagnosis.

Effects of fiscal support for agriculture on grain production technical efficiency: empirical evidence from Chinese farms

ABSTRACT This study utilizes micro-level farm panel data from 2007 to 2012 to measure the technical efficiency of grain production among farms using a stochastic frontier analysis model. Additionally, it employs a two-way fixed effects model to empirically investigate the impact of fiscal support for agriculture on grain production technical efficiency and its underlying mechanisms. The results reveal significant room for improvement in the technical efficiency of grain production among Chinese farms, with increased fiscal support for agriculture demonstrating a substantial enhancement of their efficiency. This promoting effect only exists in the major grain-producing areas and increases with the increase of farm size. The augmentation of fiscal support for agriculture achieves this goal by augmenting modern agricultural input factors, alleviating financing constraints faced by farms, and optimizing agricultural production infrastructure. Our findings provide guidance for optimizing fiscal policy to support agriculture, promoting agricultural modernization, and achieving food security.

Research on Flexible workshop Scheduling Based on harmony search Algorithm

The purpose of this paper is to use the harmony search algorithm to solve the scheduling problem of flexible job shop, with the goal of minimizing the maximum completion time. The issue of flexible workshop scheduling is an important research content in the field of production operation management. It involves how to rationally arrange the processing sequence of different jobs on different machines to optimize production efficiency. As a heuristic optimization algorithm, the harmony search algorithm performs well in solving complex optimization problems with its good global search ability and robustness. This paper first establishes a mathematical model of the scheduling problem of flexible job shop, and expounds in detail the basic principle and process of the harmonic search algorithm. Subsequently, we designed a scheduling strategy based on the harmony search algorithm, and by adjusting the algorithm parameters and strategies, we realized the effective optimization of the maximum completion time. The experimental results show that the algorithm can significantly reduce the maximum completion time and improve production efficiency when solving the scheduling problem of flexible work workshops. The research in this paper not only provides a new solution method for the scheduling problem of flexible job shop, but also provides a useful reference for the application of harmonic search algorithm in similar problems.