Product Assembly Research Articles

Putting a Kink in HIV-1 Particle Infectivity: Rocaglamide Inhibits HIV-1 Replication by Altering Gag-Genomic RNA Interaction.

Our examination of RNA helicases for effects on HIV-1 protein production and particle assembly identified Rocaglamide (RocA), a known modulator of eIF4A1 function, as an inhibitor of HIV-1 replication in primary CD4+ T cells and three cell systems. HIV-1 attenuation by low-nM RocA doses was associated with reduced viral particle formation without a marked decrease in Gag production. Rather, the co-localization of Gag and HIV-1 genomic RNA (gRNA) assemblies was impaired by RocA treatment in a reversible fashion. Ribonucleoprotein (RNP) immunoprecipitation studies recapitulated the loss of Gag-gRNA assemblies upon RocA treatment. Parallel biophysical studies determined that neither RocA nor eIF4A1 independently affected the ability of Gag to interact with viral RNA, but together, they distorted the structure of the HIV-1 RNP visualized by electron microscopy. Taken together, several lines of evidence indicate that RocA induces stable binding of eIF4A1 onto the viral RNA genome in a manner that interferes with the ordered assembly of Gag along Gag-gRNA assemblies required to generate infectious virions.

GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries.

Golden Gate assembly (GGA) can seamlessly generate full-length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost-effective libraries has been challenging. We present GGAssembler, a graph-theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one-pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a Google Colab notebook at https://github.com/Fleishman-Lab/GGAssembler.

MiRNA-193a-mediated WT1 suppression triggers podocyte injury through activation of the EZH2/β-catenin/NLRP3 pathway in children with diabetic nephropathy

Diabetic nephropathy (DN), an eminent etiology of renal disease in patients with diabetes, involves intricate molecular mechanisms. Recent investigations have elucidated microRNA-193a (miR-193a) as a pivotal modulator in DN, although its precise function in podocyte impairment remains obscure. The present study investigated the role of miR-193a in podocyte injury via the WT1/EZH2/β-catenin/NLRP3 pathway. This study employed a comprehensive experimental approach involving both in vitro and in vivo analyses. We utilized human podocyte cell lines and renal biopsy samples from pediatric patients with DN. The miR-193a expression levels in podocytes and glomeruli were quantified via qRT‒PCR. Western blotting and immunofluorescence were used to assess the expression of WT1, EZH2, β-catenin, and NLRP3 inflammasome components. Additionally, the study used luciferase reporter assays to confirm the interaction between miR-193a and WT1. The impact of miR-193a manipulation was observed by overexpressing WT1 and inhibiting miR-193a in podocytes, followed by analysis of downstream pathway activation and inflammatory markers. We found upregulated miR-193a in podocytes and glomeruli, which directly targeted and suppressed WT1, a crucial podocyte transcription factor. WT1 suppression, in turn, activated the EZH2/β-catenin/NLRP3 pathway, leading to inflammasome assembly and proinflammatory cytokine production. Overexpression of WT1 or inhibition of miR-193a attenuated these effects, protecting podocytes from injury. This study identified a novel mechanism by which miR-193a-mediated WT1 suppression triggers podocyte injury in DN via the EZH2/β-catenin/NLRP3 pathway. Targeting this pathway or inhibiting miR-193a may be potential therapeutic strategies for DN.

Digital twin technology facilitates precision improvement in complex product assembly: A progressive deduction method of data-driven tolerance allocation

Advancements in aerospace technology have significantly increased the demands on assembly precision, particularly for large aircraft models. These developments require sub-millimeter precision to be achieved in assembly, posing new challenges for conventional tolerance allocation methods, which rely heavily on theoretical models and static simulations. To address these limitations, this paper proposes a digital twin system for online deduction of assembly tolerances (DTS-ODTA) using point cloud registration and free-form deformation (FFD). The proposed method includes three key innovations. First, a digital twin (DT)-based assembly process control framework supports the simulation of assembly deviations by integrating virtual and actual data. Second, a data-driven method models the actual surface shapes, ensuring synchronization between physical and digital states. Third, a progressive deduction strategy for tolerance allocation combines virtual and actual information, providing effective inputs for real-time assembly process control. This approach was validated through an industrial case study, achieving a prediction accuracy of 91.64 % for assembly precision and effectively preventing out-of-tolerance issues during product assembly. By promptly identifying and addressing potential assembly discrepancies, this method enhances the overall assembly quality and provides critical support for design decisions. In summary, this paper introduces a novel DT-based tolerance allocation method that shifts from traditional open-loop design to closed-loop control, thereby meeting the high precision demands of modern aerospace manufacturing.

The Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research: Introduction to the 2023 Competition and Some History on the Prize’s 25th Anniversary

The judges for the 2023 competition selected the three finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning submission describes the design and deployment of an approach to accommodate the assembly of semiconductor products using multidie packages, a powerful and innovative approach to manufacturing a wide range of strategically designed semiconductors. The authors refer to the problem as the sort-assemble-blend routing problem (SABR-P), and their approach to solving the SABR-P is a combination of genetic algorithms, Monte Carlo simulations, linear programming, and machine learning. The remaining papers describe an integrated approach to system-wide deployment of nurses that facilitates the movement of nurses between hospitals, and a multiple-stage process that consolidates parcels ordered by the same consumer from one or more merchants during the fulfillment process. In addition, we provide some historical perspective on the Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research. We show the titles, abstracts, and names and affiliations for its recipients for each year in which the prize has been awarded. Finally, we provide links to the winning papers, which have been published by INFORMS Journal on Applied Analytics (formerly Interfaces). Supplemental Material: Full presentation videos with slides are available in the INFORMS Video Library at https://www.informs.org/Resource-Center/Video-Library and as electronic companions to the INFORMS Journal on Applied Analytics articles.

Bioturbation effect of high - Yield pyrazine strain on the microbial community and flavour metabolites of fortified Daqu

In order to comprehensively explore the regulatory effect of bioturbation on the assembly and overall metabolic characteristics of Daqu microbial community. This study used metatranscriptomics and metabolomics to investigate the composition of the microbial community, physicochemical properties, and overall metabolic profile of Daqu before and after bioturbation by Bacillus velezensis. The results demonstrated that the acidity, moisture, amino acid nitrogen, and starch content of Daqu exhibited a significant increase following bioturbation. The symbol microorganisms of Daqu have also changed significantly, from the original Aspergillus chevalieri, Rasamsonia emersonii, Aspergillus ruber, Aspergillus glaucus, Aspergillus wentii and Aspergillus cristatus to Paecilomyces varioti, Trichomonascus ciferrii and Staphylococcus gallinarum. Correlation analyses and integration pathways indicated that altered microbial community abundance also promoted the metabolism of starch, ethanol, fatty acids, and pyrazine, resulting in a significant enhancement of Daqu's saccharification and liquefaction power, as well as the content of some volatile and non-volatile metabolites, with pyrazines increased by 24.50%. The application of fortified Daqu to Baijiu fermentation can significantly enhance the content of various pyrazines in Baijiu. These results are helpful to elucidate the mechanism of biochemical changes, microbial assembly and flavor production of Daqu after biodisturbance.

The Inhibitory Impact of a Co-Assembly Gel with Natural Carrier-Free Binary Small Molecules, as Used in Traditional Chinese Medicine, on the Viability of SW1990 Cells.

Chinese herbs are a huge treasure trove of natural products and an important source of many active molecules. The theory of traditional Chinese medicine compatibility (TCMC) is widely applied in clinical practice, but its mechanism is still ambiguous. This study aims to open a new window for this predicament by studying the interaction between the main active ingredients from a drug pair. Carrier-free assembly of natural products improves the shortcomings of traditional nanodelivery systems and opens a new path for the development of new nanomaterials. The drug pair "Pueraria and Hedyotis diffusa" has been commonly used in clinical practice, with a predominant therapeutic effect. This study is devoted to the study of the binary small molecule co-assembly of the main active molecules from the drug pair. In this study, we introduce a carrier-free composite gel, formed by the co-assembly of puerarin (PUE) and deacetylasperulosidic acid (DAA) via non-covalent bonds including π-π packing, intermolecular hydrogen bonding, and C=O π interactions. With a strain point 7-fold higher than that of P gel, the P - D gel exhibited favorable rheological properties. The survival rate of SW1990 cells in the P - D group was only 21.39% when the concentration of administration reached 200 μM. It thus demonstrated activity in inhibiting SW1990 cells' survival, suggesting potential in combating pancreatic cancer. Furthermore, this research offers a valuable concept for enhancing the mechanical properties and bioactivity of hydrogel materials through the utilization of a multi-component natural small molecule co-assembly approach. More importantly, this provides new ideas and methods for the treatment of pancreatic cancer and the analysis of traditional Chinese medicine compatibility theory.

A digital twin-based assembly model for multi-source variation fusion on vision transformer

For the manufacture and assembly of the mechanical products, quality management is the key feature to improve the service performance, reduce the overall costs and enhance the sustainability of the manufacturing systems. While, with the emergence of advanced data-driven and digital twin technologies, the Zero-Defect Manufacturing (ZDM), which corrects, predicts, and prevents product defects based on multi-sources of data, is of great significance in improving assembly quality. As the specific application for the utilization of ZDM in product assembly, the critical performance index of the assembly results is predicted by taking into account the multi-source factors such as geometric deviation of the parts, material properties, assembly sequences and process boundary conditions during assembly. In this paper, we address the high computational cost and low computational efficiency of numerical simulation methods under multi-source factors, and propose a data-driven approach named DTA-VIT based on the fusion of heterogeneous variables for digital twin assembly modeling of products. Firstly, the geometric and performance variables of the assembly process are analyzed and modelled. Secondly, a multi-source assembly data fusion network under the Vision Transformer framework is developed. This network takes the parameter space, which fuses multi-source variables from the assembly process as input and the assembly result as output. Finally, a case study of the assembly process of composite bolted joint structures in aircraft assembly is conducted to verify the effectiveness and feasibility of the proposed method. The methodology provides a solid foundation for subsequent assembly quality control and prevent by predicting assembly performance efficiently, ultimately enabling the production of high-quality products.

Assembly Simulation and Optimization Method for Underconstrained Frame Structures of Aerospace Vehicles

Aerodynamic contour dimensional accuracy is very important for the stable and safe flight of aerospace vehicles. Nevertheless, due to the influence of various factors such as material properties, machining and manufacturing deviations, and assembly and installation deviations, key structural geometric dimensions are frequently exceeded. Therefore, this paper investigates a data-driven combined vector loop method (VLM)–Skin Model Shapes (SMS) method to realize aerospace vehicle structural geometric accuracy analysis; assembly optimization targeting contour deviation is also achieved. Tests are carried out on a typical aerospace vehicle’s underconstrained structural workpieces to validate the effectiveness of the proposed method.

Appendage regeneration requires IMPDH2 and creates a sensitized environment for enzyme filament formation.

Regeneration of lost tissue requires biosynthesis of metabolites needed for cell proliferation and growth. Among these are the critical purine nucleotides ATP and GTP. The abundance and balance of these purines is regulated by inosine monophosphate dehydrogenase 2 (IMPDH2), which catalyzes the committing step of GTP synthesis. IMPDH2 assembles into filaments that resist allosteric inhibition under conditions of high GTP demand. Here we asked whether IMPDH2 is required in the highly proliferative context of regeneration, and whether its assembly into filaments takes place in regenerating tissue. We find that inhibition of IMPDH2 leads to impaired tail regeneration and reduced cell proliferation in the tadpole Xenopus tropicalis. We find that both endogenous and fluorescent fusions of IMPDH2 robustly assemble into filaments throughout the tadpole tail, and that the regenerating tail creates a sensitized condition for filament formation. These findings clarify the role of purine biosynthesis in regeneration and reveal that IMPDH2 enzyme filament formation is a biologically relevant mechanism of regulation in vertebrate regeneration.

Optimal procurement decisions based on smoothing strategy for dual sourcing with different lead times

This study establishes a discrete-time inventory model with a smoothing procurement strategy in dual sourcing. Specifically, an assembly manufacturer purchases components from two suppliers: one overseas supplier with a long lead time but a low price, and one local supplier with a short lead time but a high price. A smoothing procurement approach taking into account previous ordering record and current demand is adopted by the assembly manufacturer. We start by exploring the scenario where storage space is not a constraint. The cost-minimization conditions for single sourcing and dual sourcing are studied. Assuming demand follows a normal distribution, the optimal order quantities and inventory levels are subsequently derived and shown to follow the same distribution. By comparing the inventory costs, it is found that dual sourcing is superior to single sourcing. Then, we examine the scenario where an assembly manufacturer with limited storage space collaborates with a third-party logistics company. The optimal order quantities, inventory levels, smoothing procurement coefficient and storage space are derived for the assembly manufacturer. Finally, numerical experiments with practical data are constructed to verify our findings.

Local buckling behaviour of Q690 high strength steel press-braked elliptical hollow section stub columns: Testing, numerical modelling, and design

This paper presents comprehensive investigations on the local bucking behaviour of Q690 high strength steel (HSS) press-braked elliptical hollow section (EHS) stub columns subject to concentric compression experimentally. In comparison with conventional hot-finished EHS or cold-rolled EHS, the press-braked EHS provides high precision, great construction productivity and the fabricated geometries are not limited to production assembly instrumentation. HSS press-braked EHS fabricated through press-braking process was expected to exhibit distinct behaviour compared with their counterparts constructed from the conventional fabrication routes. Well-understanding of their performance at the levels of material, and cross-section level is essential. Material properties were obtained through tensile coupon tests. Residual stresses in both membrane and bending types were measured and recorded and their impact on structural performance was also assessed. Initial local geometric imperfection measurements were conducted for all the specimens. In conjunction with experimental studies, the finite element (FE) models of Q690 HSS press-braked EHS stub columns were developed. The accuracy of the FE model for HSS press-braked EHS stub column was asserted by comparing the numerical predictions with the tested results. An extensive parametric study was conducted to generate structural performance data with a wider range of cross-section slenderness to complement the test data bank. The cross-section classification for conventional CHS codified in existing standards and the proposed slenderness limits by various researchers were evaluated. The existing slenderness limits cannot be extended to HSS press-braked EHS investigated in this study, a new slenderness limit is proposed. Moreover, the advanced design approaches of Direct Strength Method (DSM) and Continuous Strength Method (CSM) were assessed for their applicability to the cross-section resistance predictions of the examined EHS stub columns.

Mouse hepatitis virus JHMV I protein is required for maximal virulence.

Betacoronaviruses encode a conserved accessory gene within the +1 open reading frame (ORF) of nucleocapsid called the internal N gene. This gene is referred to as "I" for mouse hepatitis virus (MHV), ORF9b for severe acute respiratory CoV (SARS-CoV) and SARS-CoV-2, and ORF8b for Middle East respiratory syndrome CoV (MERS-CoV). Previous studies have shown ORF8b and ORF9b have immunoevasive properties, while the only known information for MHV I is its localization within the virion of the hepatotropic/neurotropic A59 strain of MHV. Whether MHV I is an innate immune antagonist or has other functions has not been evaluated. In this report, we show that the I protein of the neurotropic JHM strain of MHV (JHMV) lacks a N terminal domain present in other MHV strains, has immunoevasive properties, and is a component of the virion. Genetic deletion of JHMV I (rJHMVIΔ57-137) resulted in a highly attenuated virus both in vitro and in vivo that displayed a post RNA replication/transcription defect that ultimately resulted in fewer infectious virions packaged compared with wild-type virus. This phenotype was only seen for rJHMVIΔ57-137, suggesting the structural changes predicted for A59 I altered its function, as genetic deletion of A59 I did not change viral replication or pathogenicity. Together, these data show that JHMV I both acts as a mild innate immune antagonist and aids in viral assembly and infectious virus production, and suggest that the internal N proteins from different betacoronaviruses have both common and virus strain-specific properties.IMPORTANCECoV accessory genes are largely studied in overexpression assays and have been identified as innate immune antagonists. However, functions identified after overexpression are often not confirmed in the infected animal host. Furthermore, some accessory proteins are components of the CoV virion, but their role in viral replication and release remains unclear. Here, we utilized reverse genetics to abrogate expression of a conserved CoV accessory gene, the internal N ("I") gene, of the neurotropic JHMV strain of MHV and found that loss of the I gene resulted in a post replication defect that reduced virion assembly and ultimately infectious virus production, while also increasing some inflammatory molecule expression. Thus, the JHMV I protein has roles in virion assembly that were previously underappreciated and in immunoevasion.

Optimum production lot size for a perishable product under exponential demand with partial backordering and rework

The management of inventory significantly impacts an organization's profitability. Numerous researchers have developed the production model tailored for decaying items to boost sales and minimize inventory, thereby increasing turnover ratios. This study focuses on the EPQ model for reworkable items under exponential combined with inventory dynamics. The primary objective is to optimize the production plan to manage maintenance costs effectively, ensuring optimal Leveraging human and technological resources. The research determines the optimal cycle length, backordered quantities, and production quantities to minimize overall costs. Specifically, penalties are incurred for excessive production times during rework and non-rework phases. This model finds applications in industries such as printed circuit board assembly in semiconductor manufacturing, metal parts, and plastic molding. The study concludes with numerical examples comparing total costs under the proposed model, supplemented by sensitivity analyses that explore the impacts of parameter variations on decision-making.

Diffusion-Bonded Unitized Anode Assemblies for PEM Electrolysis Stacks

Hydrogen generation via proton exchange membrane (PEM) water electrolysis is in the midst of a technological renaissance aimed at achieving cost parity with steam reforming within the decade. Reaching that plateau will require substantial system cost reductions accompanied by increased performance of the cell stack module. Many proposed improvements to the cell stack draw from the established principles of electrochemical and manufacturing engineering, targeting lower component resistances while lowering part count and enhancing ease of assembly, respectively.The PEM electrolyzer anode assembly (comprising the porous transport layer (PTL) and the anode flow field) is suited for just such a revision. In practice, the anode PTL is positioned on a metallic bipolar plate that is machined or embossed with a flow field, or the PTL is joined to an assemblage of thin macroporous media that serves as a flow field1. In both cases, the interfacial resistances between the subcomponents of the assembly are non-negligible and are conventionally mitigated by applying noble metal coatings, typically Pt.In this contribution, we investigate alternatives to conventional assemblies via diffusion bonding. Using thermal treatments, the porous transport layer is joined to the anode flow field, yielding a robust, atomic-scale metallurgical bond. We demonstrate the manufacturing of large-format assemblies in multiple configurations, using either stacked microporous media or grooved plates, meeting tight tolerances for thickness across their span. We also show that the bulk-like character imparted to the assembly obviates the need for antipassivation coatings at the critical interfaces between components, evinced by low series resistance in both ex situ and in situ electrolysis cell tests.References(1) Dristy, M. E. Proton Exchange Membrane Electrochemical Cell System. US7270908B2, September 18, 2007.

Assessment of a large language model based digital intelligent assistant in assembly manufacturing

The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating the technical robustness of DIAs, assessing their effect on operators' cognitive workload and user experience, and determining the overall performance improvement of the assembly process. Methodologically, the research employs a laboratory experiment, incorporating a controlled setting to meticulously assess the DIA's performance. The experiment used a between-subjects design comparing a group of participants using the DIA against a control group relying on traditional manual methods across a series of assembly tasks. Findings reveal a significant enhancement in the operators' experience, a reduction in cognitive load, and an improvement in the quality of process outputs when the DIA is employed. The article contributes to the study of the DIA's potential and AI integration in manufacturing, offering insights into the design, development, and evaluation of DIAs in industrial settings.

Augmented Reality and Virtual Reality Applications: Enhancing User Experience Across Industries

Augmented Reality (AR) and Virtual Reality (VR) technologies have emerged as powerful tools for enhancing user experiences across various industries. This article explores the applications of AR and VR and their impact on user experiences in diverse sectors. Employing a qualitative method, this study conducts a literature review through library research to examine the current trends and developments in AR and VR applications. The findings reveal that AR and VR technologies have been increasingly adopted across industries such as education, healthcare, entertainment, manufacturing, and retail, among others. In education, AR and VR offer immersive learning experiences, allowing students to interact with digital content in a simulated environment. In healthcare, these technologies enable medical professionals to visualize complex anatomical structures and perform virtual surgeries, leading to improved diagnostic accuracy and patient outcomes. In the entertainment industry, AR and VR provide users with immersive gaming and storytelling experiences. In manufacturing, AR and VR enhance product design, prototyping, and assembly processes, leading to increased efficiency and reduced costs. Lastly, in retail, AR and VR applications enable virtual product try-ons, personalized shopping experiences, and interactive product showcases, thereby enhancing customer engagement and satisfaction. Overall, AR and VR technologies offer promising opportunities to revolutionize user experiences across industries, although challenges such as hardware limitations, content creation, and user acceptance need to be addressed for wider adoption

Design for assembly in construction system: three iterative upgrades of a panelized system

ABSTRACT As a scientific design methodology, Design for Assembly (DFA) aims to enhance product assembly efficiency through the design simplification. Checkered Playroom, a children's activity building designed by Unitinno Architecture Studio, has been constructed in over 160 times in China, undergoing three iterations of design. This paper begins with a brief review of the development of DFA concepts, and then provides a detailed discussion of the design, composition and construction of the three generations of Checkered Playrooms. Finally, through an analysis of the assembly design of the three generations of Checkered Playrooms, design strategies for improving assembly efficiency are discussed, including the reduction of part quantity, the increase of module size, the improvements in module shapes, and the simplification of connection joints. These strategies can also be applied to other building system designs to enhance assembly efficiency.

A self-learning particle swarm optimization for bi-level assembly scheduling of material-sensitive orders

In modern assembly production, maximizing fulfillment becomes challenging amid resource scarcity and process unpredictability. This study addresses such issues with an effective approach to solving the integrated problem. This confluence presents a scenario where resource allocation mirrors a multidimensional knapsack and job sequencing corresponds to distributed assembly permutation flowshop scheduling, factoring in sequence-dependent setup times. Employing a mixed-integer mathematical model, we capture the configurations and objectives of the integrated problem. A special dual-level cooperative self-learning particle swarm optimization (SLPSO) metaheuristic is developed for improved hierarchical decision-making. Adaptive learning is harnessed to guide dynamic job selection under uncertainty, aiming for the highest total value. To minimize tardiness, a neighborhood search operator customizes sequences for each shop, considering changing setup times between jobs. Benchmark tests have been performed, showing that SLPSO surpasses established genetic algorithms and swarm techniques by 8%–15%. When implemented in an operational traditional pharmaceutical manufacturer, significant improvements were observed. The integrated structure harmonizes planning and scheduling stages, enhancing the algorithm via bidirectional feedback between decisions at various levels under uncertainty, thus providing guidance for real-world production.

Two-sided resource-constrained assembly line balancing problem: a new mathematical model and an improved genetic algorithm

Two-sided assembly lines are typically employed in the production of medium and large-sized products with the aim of reducing the length of the assembly line, enhancing assembly efficiency and consequently reducing the time required for product assembly. However, traditional Two-sided assembly lines lack effective resource scheduling management methods in production scheduling, which results in low productivity and high resource costs. In order to address this issue, we propose a new two-sided resource-constrained assembly line balancing problem (TRCLBP) model. The model takes the minimum number of workstations and the minimum assembly cost as its objective function and proposes an improved genetic algorithm (I-GA) to solve it. A three-layer chromosome initialization method is proposed for the assembly tasks and resource decisions, which effectively improves the diversity and quality of the initial population. Furthermore, the algorithm employs strategies such as matching crossover and redistributing variants to ensure rapid convergence of the populations and to prevent them from falling into local optimums. Finally, the efficacy of the model and algorithm proposed in this paper is validated through a comprehensive analysis of arithmetic case studies and enterprise engineering examples. This analysis reveals a reduction of approximately 18 % in the total cost of assembly. Furthermore, the model enables enterprises to make informed decisions regarding the optimal allocation of resources, thereby reducing production costs and improving the efficiency of assembly operations during periods of expansion.