Cellular Manufacturing Research Articles

IMMU-34. PREDICTION OF TUMOR-ASSOCIATED ANTIGENS USING RADIOGENOMICS

Abstract INTRODUCTION Glioblastoma remains a disease with dismal prognosis. Immunotherapy demonstrated promising results against some refractory cancers. However, comparative results have not been accomplished in glioblastoma therapy due to lack of suitable tumor targets and considerable clonal heterogeneity. OBJECTIVE We propose leveraging radiogenomics using machine learning based on Pre-Op MRI to predict tumor antigen expression profiles displaying specific targets for immunotherapy. METHODS We collected RNAseq data and Pre-Op Axial T2-FLAIR images in 29 patients from IVY Glioblastoma Atlas Project. MRIs were segmented using LIFEx software as follows: 1) tumor + edema (T+E) and 2) whole brain (WB). The intended prediction was for tumor-associated antigens. H2O.ai software was used for prediction using an automated supervised machine learning algorithm. Antigens that achieved >55% area under the curve (i.e., prediction) were reanalyzed adjusting for reproducibility, time of analysis and maximum precision. RESULTS An average of 53.2 T2-FLAIR axial slices per patient were used for 3D reconstruction. We selected the following genes (ERBB2, TP53, IL10RA, CD163, EGFR and MGMT) for downstream analysis given their clinical, immunologic, and prognostic relevance. Regarding tumor-associated antigen prediction for ERBB2, TP53, IL10RA, CD163, EGFR and MGMT predicted 52%, 56%, 66%, 66%, 56% and 0%, on T+E segmentation respectively; The values were 49%, 93%, 87%, 47%, 37% and 0% for the respective WB group. Reanalysis revealed 100% prediction accuracy for the presence of mutant TP53 and IL10RA in both segmentation groups (T+E, WB). CD163 and EGFR prediction was 54% and 61% for the T+E group and 48% for both antigens in the WB group. CONCLUSION We successfully predicted the presence of two out of six antigens (i.e., mutant TP53 and IL10RA) in this patient cohort. Our approach can provide expeditious antigen identification to enable cellular therapy manufacturing for patients with unresectable newly diagnosed tumorsor those which relapsed geographically. Furthermore, this MRI-based methodology might provide a precision medicine approach to places that cannot afford costly testing (i.e., RNAseq).

CT584 Is Not a Protective Vaccine Antigen against Respiratory Chlamydial Challenge in Mice.

Background:Chlamydia trachomatis is the most prevalent bacterial sexually transmitted pathogen in humans worldwide. Since chlamydial infection is largely asymptomatic with the potential for serious complications, a preventative vaccine is likely the most viable long-term answer to this public health threat. Cell-free protein synthesis (CFPS) utilizes the cellular protein manufacturing machinery decoupled from the requirement for maintaining cellular viability, offering the potential for flexible, rapid, and decentralized production of recombinant protein vaccine antigens. Methods: Here, we use CFPS to produce the full-length putative chlamydial type three secretion system (T3SS) needle-tip protein, CT584, for evaluation as a vaccine antigen in mouse models. High-speed atomic force microscopy (HS-AFM) (RIBM, Tsukuba, Japan) imaging and computer simulations confirm that CFPS-produced CT584 retains a native-like structure prior to immunization. Female mice were primed with CT584 adjuvanted with CpG-1826 intranasally (i.n.) or CpG-1826 + Montanide ISA 720 intramuscularly (i.m.), followed four weeks later by an i.m. boost before respiratory challenge with 104 inclusion forming units (IFU) of Chlamydia muridarum. Results: Immunization with CT584 generated robust antibody responses but weak cell-mediated immunity and failed to protect against i.n. challenge as demonstrated by body weight loss, increased lung weights, and the presence of high numbers of IFUs in the lungs. Conclusion: While CT584 was not a protective vaccine candidate, the speed and flexibility with which CFPS can be used to produce other potential chlamydial antigens make it an attractive technique for antigen production.

A hybrid data-driven optimization and decision-making approach for a digital twin environment: Towards customizing production platforms

In the Industry 4.0 era, advanced technologies are transforming manufacturing processes and systems. Additionally, the increasing prevalence of big data and AI technologies have made decision-making using manufacturing data increasingly important. However, Small and Medium-sized Enterprises (SMEs) have encountered significant obstacles in adopting these technologies due to resource limitations and constraints. For SMEs, selecting an appropriate production strategy is challenging due to the complexity of manufacturing systems. As a response, this paper proposes a hybrid Simulation-Optimization with Multi-Criteria Decision-Making (SOMCDM) framework for SMEs to identify effective and customized production layouts. In the proposed approach, we model various production scenarios using a cellular manufacturing system. Surrogate models for different production layouts are created to basis functions using Multivariate Adaptive Regression Splines (MARS). Subsequently, the basis functions are used as fitness functions to identify optimal production parameters in a genetic algorithm. Then, optimized parameters are applied to production criteria and ranked using a multi-criteria decision-making technique. In a case study, the proposed framework is applied to select the best production platform among three scenarios for a company assembling complex products. The selected production platform improves overall manufacturing performance by 11.95% compared to the existing one. This study demonstrates the effectiveness of the proposed framework in identifying the best production platform for labor-intensive SMEs manufacturing high-mix, low-volume products using SOMCDM for a digital twin environment. The proposed framework is further detailed through a case study of a 3D printer assembly factory.

3D Printing as a Strategy to Scale-Up Biohybrid Hydrogels for T Cell Manufacture.

The emergence of cellular immunotherapy treatments is introducing more efficient strategies to combat cancer as well as autoimmune and infectious diseases. However, the cellular manufacturing procedures associated with these therapies remain costly and time-consuming, thus limiting their applicability. Recently, lymph-node-inspired PEG-heparin hydrogels have been demonstrated to improve primary human T cell culture at the laboratory scale. To go one step further in their clinical applicability, we assessed their scalability, which was successfully achieved by 3D printing. Thus, we were able to improve primary human T cell infiltration in the biohybrid PEG-heparin hydrogels, as well as increase nutrient, waste, and gas transport, resulting in higher primary human T cell proliferation rates while maintaining the phenotype. Thus, we moved one step further toward meeting the requirements needed to improve the manufacture of the cellular products used in cellular immunotherapies.

A modified bi-objective NSGA-II approach to sustainability in reconfiguration planning of dynamic cellular manufacturing systems

Manufacturing plant layouts are developed to facilitate optimal process flow. Modern manufacturing systems must meet present production demands and be adaptable to changes in process flow in the future. Dynamic Cellular Manufacturing Systems (DCMS) increase the flexibility of layouts by reconfiguring cell structure and equipment distribution, to effectively adjust part routings for optimal process flow. Frequent reconfiguring of plant layout may not always be feasible or economical, however, when new product releases are planned, reconfiguring the plant layout to optimise the work-flow may be extremely beneficial. This paper presents a Non-dominated Sorting Genetic Algorithm (NSGA-II) approach to solving a DCMS problem in a sustainable, and responsible manner. A bi-objective integer programming model was developed over multiple planning horizons with fluctuating product demands. This model aims to achieve sustainability by reducing the cost of production, mitigating the environmental impact of production, and minimise negative social impacts on labourers that work in such environments. A penalty function approach was used to enforce the model constraints during optimisation. This study details trade-offs between the economic factors of a DCMS, the environmental implications of reconfiguring such a system, and the social impacts of reconfigurations on the workforce.

Cell Formation Problem with Alternative Routes and Machine Reliability: Review, Analysis, and Future Developments

The Cell Formation Problem (CFP) is a widely studied issue that aims to group machines effectively based on criteria such as productivity, lower costs, and greater efficiency. In recent years, more characteristics were summarized relating to this problem. This paper provides a bibliographic examination of methodologies addressing the CFP in cellular manufacturing, focusing on novel approaches such as alternative routes and machine reliability. The articles were obtained from Scopus and Web of Science and filtered using the PRISMA methodology. Classification based on objective functions, constraints, and methodologies facilitated informative visualizations for analysis. Findings indicate a focus on capital utilization optimization, with cost reduction via intercellular moves minimization as the primary objective. Common constraints include limits on the number of machines per cell, restricting machines to a single cell and singular production routes per part. The genetic algorithm predominates as a non-exact solution approach, while the “ε-constraint” method is commonly used. This study offers insights into contemporary trends in solving the CFP with alternative routings and machine reliability, aiding researchers and professionals in the field to improve the quality of their investigations.

A graph-pair representation and linear programming embedded genetic algorithm for unequal-sized layout of cellular manufacturing systems

A graph-pair representation and linear programming embedded genetic algorithm for unequal-sized layout of cellular manufacturing systems

Phase-type distribution models for performance evaluation of condition-based maintenance

ABSTRACT Condition-based maintenance (CBM) is gaining attention due to sensor and cloud-based analytics advancements, but research on its impact on system-level performance is limited. Insufficient understanding during CBM implementation can lead to confidence issues and failures. This study introduces a class of models using phase-type distribution to assess three maintenance strategises: run-to-failure (RTF), time-based preventive maintenance (TBM), and CBM. Employing machine health-index, the framework characterizes production performance by estimating effective process times. The model demonstrates how adjusting CBM thresholds influences process time variations and assesses the impact of changing maintenance frequency for TBM. Applied to a smart cellular manufacturing system, the model shows CBM’s early-stage implementation. Findings indicate CBM with optimized thresholds boosts maximum throughput by 6.77%. Further, CBM achieves an additional 6.84% increase assuming corrective maintenance time can be reduced by 20%. This approach can help manufacturing become smarter through smarter maintenance in the Industry 4.0 era and beyond.

Evaluation of Performance Enhancement in Garment Industry Using Lean Tools

The comprehensive idea of lean manufacturing emphasizes the identification and removal of wastes related to production. Increased productivity, shorter lead times, improved line balancing, lower work in progress (WIP), higher quality, more adaptable designs, lower costs, etc. are all major objectives of applying lean tools. This study aims to investigate how lean manufacturing techniques might enhance manufacturing efficiency in Bangladeshi RMG sector. The sewing section of a small scale garment industry was the focus of this study. In the study, the existing line layout of a trouser style was examined using value stream mapping which contributed to the identification of numerous waste in the existing layout. In order to change the current status of the sewing section, the workplace was organized by 5S tools to remove wastes identified by VSM followed by altering the conventional longer line layout into three cell of work station as suggested by cellular manufacturing. After implementation of these lean tools, results observed include, production output is increased by 17.80%, efficiency is increased by 12.46%, SMV of product is standardized to 18.68 min., defects per hundred units is reduced to 10% and order change over time is reduced by 37%. The conclusions reached during the execution of this implementation study are realistic and applicable in industries with similar structures. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 10(1), 2023 P 27-36

Financial Management Strategies for Implementing Lean Manufacturing in the Footwear Industry

The concept of lean manufacturing, initially developed to maximize resource utilization by minimizing waste, has evolved to address the needs of a rapidly changing and competitive business environment. Footwear industries face significant challenges and complexities that require a systematic and continuous response to maintain and enhance product value. Implementing lean manufacturing practices has become essential for these organizations to sustain and thrive in such a dynamic landscape. However, successful implementation is not solely dependent on operational adjustments but also on strategic financial management. This research examines the financial management strategies necessary for the effective implementation of lean manufacturing in the footwear industry. It emphasizes the importance of integrating financial planning with lean principles to ensure comprehensive and sustainable improvements. Key lean elements such as Value Stream Mapping (VSM), Cellular Manufacturing (CM), U-line systems, Line Balancing, Inventory Control, Single Minute Exchange of Dies (SMED), Pull Systems, Kanban, and Production Levelling are explored in detail. The study demonstrates how a focused approach to financial management can support these lean elements, enabling footwear companies to optimize resource allocation, reduce costs, and improve efficiency. By aligning financial strategies with lean manufacturing goals, organizations can achieve significant economic and operational benefits. This includes better cash flow management, investment in continuous improvement initiatives, and a robust framework for measuring the financial impact of lean practices. Through a comprehensive analysis of these financial strategies, the research provides valuable insights for footwear companies aiming to implement lean manufacturing successfully. It highlights the need for an integrated approach that combines financial acumen with lean methodologies to drive value creation, enhance competitiveness, and ensure long-term sustainability in the industry.

Evaluation in mice of cell-free produced CT584 as a Chlamydia vaccine antigen.

Chlamydia trachomatis is the most prevalent bacterial sexually transmitted pathogen worldwide. Since chlamydial infection is largely asymptomatic with the potential for serious complications, a preventative vaccine is likely the most viable long-term answer to this public health threat. Cell-free protein synthesis (CFPS) utilizes the cellular protein manufacturing machinery decoupled from the requirement for maintaining cellular viability, offering the potential for flexible, rapid, and de-centralized production of recombinant protein vaccine antigens. Here, we use CFPS to produce the putative chlamydial type three secretion system (T3SS) needle-tip protein, CT584, for use as a vaccine antigen in mouse models. High-speed atomic force microscopy (HS-AFM) imaging and computer simulations confirm that CFPS-produced CT584 retains a native-like structure prior to immunization. Female mice were primed with CT584 adjuvanted with CpG-1826 intranasally (i.n.) or CpG-1826 + Montanide ISA 720 intramuscularly (i.m.), followed four-weeks later by an i.m. boost before respiratory challenge with 104 inclusion forming units (IFU) of Chlamydia muridarum. Immunization with CT584 generated robust antibody responses but weak cell mediated immunity and failed to protect against i.n. challenge as demonstrated by body weight loss, increased lungs' weights and the presence of high numbers of IFUs in the lungs. While CT584 alone may not be the ideal vaccine candidate, the speed and flexibility with which CFPS can be used to produce other potential chlamydial antigens makes it an attractive technique for antigen production.

Simultaneous consideration of time and cost impacts of machine failures on cellular manufacturing systems

Cellular manufacturing systems (CMS) are essential for achieving efficient production processes, and their success relies on effective cell formation and layout design. This research paper presents a novel nonlinear mathematical programming model that employs the rectilinear distance notion to define the layout within a continuous space. The proposed model incorporates the stochastic nature of machine failures, considering the unreliability with the inclusion of a stochastic time between failures. With a bi-objective approach, the model aims to minimize inter and intra-cell movements of parts, as well as the associated costs related to exceptional elements (EEs), cell reconfigurations, and machine failures. To optimize the proposed model, several multi-objective meta-heuristic algorithms, including the multi-objective particle swarm optimization (MOPSO), multi-objective taboo search (MOTS), and non-dominated sorting genetic algorithm (NSGA-II), are introduced. The effectiveness of these algorithms is validated through numerical instances and a real case study. The results indicate that the MOPSO algorithm exhibits superior performance in optimizing various multi-objective criteria. The presented mathematical model and algorithms provide valuable tools for manufacturers to optimize their CMS, resulting in reduced costs, enhanced production efficiency, and increased competitiveness. By considering the simultaneous effects of time and cost associated with machine failures, this research offers practical insights and solutions for improving the performance of cellular manufacturing systems.

Design of an improved layout for a steel processing facility using SLP and lean Manufacturing techniques

Implementing lean manufacturing practices in the systematic layout planning (SLP) process for improving facility layouts in various industries is essential. This approach enhances material handling processes and effectively manages space utilization. An efficient plant employing systematic layout planning leads to improved resource utilization and facilitates the integration of lean manufacturing techniques such as value stream mapping, 5 S, Ohno’s 7 wastes, and cellular manufacturing. This research article presents a case study on a steel processing plant in Dubai with an area of 160 m × 160 m, where systematic layout planning and lean manufacturing techniques were applied. The factors considered included space and material flow optimization, incorporating layout alternatives based on systematic layout planning principles along with lean manufacturing techniques. The study revealed a 34% reduction in material flow and an efficient overall space utilization improvement of 26%. Additionally, cost analysis validation was conducted.

Maximizing Group Efficacy for the Generalized Cell Formation Problem Using Route Rank Index and Genetic Algorithm

Cell formation plays a crucial role in the development of cellular manufacturing systems (CMS). Previous studies in this field have typically assumed that each part is associated with a single process plan. However, incorporating alternative routes offers additional flexibility in CMS design. This paper addresses the cell formation problem by considering alternative routes and presents a two-stage approach to address this problem. In the first stage, a Route Rank Index (RRI) is developed based on a correlation matrix to select the optimal alternative route for each part. Subsequently, a Genetic Algorithm (GA) is employed in the second stage to form part families and machine cells. The proposed approach's computational performance is evaluated using a set of generalized group technology datasets found in the existing literature. The results demonstrate that the proposed approach is highly effective and efficient when it comes to addressing the cell formation problem involving alternative routes. The ramifications of these findings in practice are substantial. Our suggested approach demonstrates its resilience and adaptability by achieving comparable or better grouping results across a wide variety of benchmark datasets. This shows the method can be used in a wide range of practical situations, including those involving matrices of varying sizes and shapes. The theoretical knowledge base on part-machine grouping strategies benefits from the comparison study. By comparing the results of our suggested method to those of well-known heuristics, we shed light on its benefits and drawbacks. Index Terms— Route Rank Index; Alternative Routes; Cellular Manufacturing System; Cell Formation; Genetic Algorithm.

Mathematical model and adaptive multi-objective evolutionary algorithm for cellular manufacturing with mixed production mode

As the product mix in production changes dramatically, cell reconfiguration is requisite to smoothen the production process. Meanwhile, multiple production modes are simultaneously adopted in the site to promote productivity and assure flexibility, and thus the coordination scheduling among them becomes a challenging problem. To achieve cell reconfiguration and cell scheduling in a cellular manufacturing system in which no-idle flow-line and flexible job-shop production modes are hybridized, a mixed integer linear programming model is formulated and an enhanced adaptive multi-objective evolutionary algorithm is developed. In the proposed algorithm, a decision tree-based rule combination selector is developed to adaptively select the most appropriate rule combination fit for the given production scenario to generate a high-quality initial population. Three types of crossover operators and six objective-oriented local search operators are designed to increase the exploration and exploitation capability. An adaptive balance mechanism of exploration and exploitation is trained by Q-learning to maximize search efficiency. In addition, an adaptive adjustment mechanism of population size is designed to ensure diversity and speed up convergence. The comparative study demonstrates that the three proposed adaptive mechanisms are effective and the proposed algorithm with three adaptive mechanisms significantly outperforms other comparison algorithms in solving the studied problem.

A New Mathematical Model for Cell Layout Problem considering Rotation of Unequal Dimensions of Cells and Machines

One of the main concepts in group technology (GT) is the cellular manufacturing system (CMS) with three main problems of cell formation (CF), cell layout (CL), and cell scheduling (CS). This paper studies the cell layout problem (CLP), aiming to find the optimal layout of machines within each cell (intracellular layout) and the optimal layout of cells in each workshop (intercellular layout). To adapt to reality, the dimensions of the cells and machines (inside each cell) were considered unequal, and also the cells and machines could rotate. We believe that a cellular layout that assumes unequal dimensions of the cells and machines can be used for batch production. This kind of production has a wide variety of low to medium demand. Furthermore, a cellular layout can be applied in CMSs and also in noncontinuous industries that have a job shop layout. Our main contribution is considering the possibility of rotating the cells and machines inside the cells. For this purpose, a mixed nonlinear programming model was developed to solve the CLP with the minimum cost of intracellular and intercellular material flows. The proposed nonlinear model was first converted into a linear model, and then a problem was generated and solved with GAMS software to validate the resulting linear model. This model finds the best layout of cells within the workshop and the best layout of machines inside each cell. Then, because of the NP-hardness of the CLP and the fact that even exact methods cannot solve large-scale examples in an acceptable computational time, an imperialist competitive algorithm (ICA) was designed and used to solve the problem. To evaluate the efficiency of the proposed algorithm, its numerical results in small dimensions were compared with the results of GAMS software. In large dimensions, 30 random problems were created, and the results of ICA were compared with the results of the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Finally, the parameters of the three meta-heuristic algorithms were set by the Taguchi method. Numerical results indicated that ICA was superior to both the PSO algorithm and GA. It could also achieve efficient solutions in a shorter computational time.

Comparison of lean cellular manufacturing organization models: Multiple case study analysis

Comparison of lean cellular manufacturing organization models: Multiple case study analysis

Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company.

Cell formation (CF) and machine cell layout are two critical issues in the design of a cellular manufacturing system (CMS). The complexity of the problem has an exponential impact on the time required to compute a solution, making it an NP-hard (complex and non-deterministic polynomial-time hard) problem. Therefore, it has been widely solved using effective meta-heuristics. The paper introduces a novel meta-heuristic strategy that utilizes the Genetic Algorithm (GA) and the Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) to identify the most favorable solution for both flexible CF and machine layout within each cell. GA is employed to identify machine cells and part families based on Grouping Efficiency (GE) as a fitness function. In contrast to previous research, which considered grouping efficiency with a weight factor (q = 0.5), this study utilizes various weight factor values (0.1, 0.3, 0.7, 0.5, and 0.9). The proposed solution suggests using the TOPSIS technique to determine the most suitable value for the weighting factor. This factor is critical in enabling CMS to design the necessary flexibility to control the cell size. The proposed approach aims to arrange machines to enhance GE, System Utilization (SU), and System Flexibility (SF) while minimizing the cost of material handling between machines as well as inter- and intracellular movements (TC). The results of the proposed approach presented here show either better or comparable performance to the benchmark instances collected from existing literature.

Time series importance measure-based reliability optimization for cellular manufacturing systems

Cellular manufacturing systems (CMSs) can improve the quality and efficiency of the manufacturing process by multiple processing cells with different functions and group technology. CMSs require high reliability to complete processing missions successively, so reliability optimization is an important part to guarantee system performance. This paper proposes a binary decision diagram-based three-step evaluation method to analyze CMS reliability. A reliability optimization model of CMS is constructed by considering the limited cost to determine the optimal combination of machine degradation parameters. Considering the advantages of ant colony optimization (ACO) and time series importance measure (TIM), a TIM-based ant colony optimization (TIACO) is developed to solve the optimization model. To verify the performance of TIACO, system reliability and running time are introduced to compare with genetic algorithm (GA), ACO, and time series importance measure-based genetic algorithm (TIGA). (1) System reliability obtained by TIACO is always the best. (2) Running time of TIACO is smaller. A case study of an unmanned aerial vehicle manufacturing company verifies the effectiveness of TIACO, and machines with higher TIMs should be given priority to improving their degradation parameters, which provides a new idea for reliability evaluation and optimization of CMSs.

Overview of Manufacturing Systems—Evolution from Group Technology to Dynamic Cellular Manufacturing System

Overview of Manufacturing Systems—Evolution from Group Technology to Dynamic Cellular Manufacturing System