Cellular Manufacturing Research Articles

Simulation-based evaluation of performance benefits from flexibility in assembly systems and matrix production

This paper contributes to a novel approach for analysing and quantifying the benefits for reconfigurable cellular manufacturing and assembly systems (RCMS/RCAS), also known as Matrix production, based on modelling and dynamic simulation.International market trends concerning consumer behaviour and demand result in increasing flexibility requirements, which conventional production lines struggle to meet, thus creating the need for flexible production and assembly systems such as Matrix production. The flexibility results in additional variables and thus a more complex system behaviour. To utilize its benefits, the design and setup of a matrix production is crucial and for potential real-world applications. However, methods to quantitatively assess beneficial features of a matrix production are only beginning to appear in the literature, making decision-making for a production system transformation challenging. This paper contributes a simulation-based approach to analyse the performance benefits of matrix production using modelling and dynamic simulation. The method is demonstrated with a simplified automotive assembly use case for a dynamic multi-variant production. The results confirm the feasibility of the method and quantify the potential for increased assembly system performance gained from specific flexibility features in a matrix production.

Causal relationship among critical factors for cellular manufacturing system using DEMATEL approach

Causal relationship among critical factors for cellular manufacturing system using DEMATEL approach

A Skill-Based MILP Model in Cellular Manufacturing Systems with Human-Robot Collaboration

Worker assignment in cellular manufacturing systems (CMS) has become more important for efficiency and flexibility under qualified workforce restrictions. Researchers primarily focus on assigning skilled workers to operations to optimize capacity and related costs. On the other hand, the advantages of automation in human-operated manufacturing cells are well known. However, some manufacturing operations require high customization and consequently depends on skilled workers. Therefore, complete automation is not desirable and a manufacturing system, with both manual and automated resources, can be the best configuration. To support this design, cobots can present suitable solutions to work side by side with humans. In this study, a stochastic mixed-integer linear programming (MILP) is introduced to minimize the total cost of robots’ operating cost and worker employment cost in CMS when assigning resources to cells which also considers technical skills and non-technical skills. The operation times of workers are considered as stochastic, while the operation times of robots are deterministic. The demands of the parts are assumed uniformly distributed and stochastic. The result for the optimization problem is presented on the small-sized test data. The proposed MILP model is solved in the GAMS modeling environment. The calculation of collaboration level for each operation is novel in this study and enables the best possible resource assignment based on skill-level. A small-sized example is used to test the impact of constraints. Results confirmed the success of the model as a sufficient tool to simultaneously assign workers and robots with skill-related constraints.

Causal Relationship among Critical Factors for Cellular Manufacturing System Using DEMATEL Approach

Causal Relationship among Critical Factors for Cellular Manufacturing System Using DEMATEL Approach

A genetic algorithm for designing an integrated cellular manufacturing system considering a linear double-row layout

To design an integrated cellular manufacturing system, cell formation (CF), cellular layout (CL), and cellular scheduling (CS) need to be determined efficiently. In this study, we propose a theoretical model for designing a cellular manufacturing system that will optimise the total completion time by making these three interrelated decisions concurrently. In the theoretical model, linear single-row and double-row cellular layouts were applied, and a genetic algorithm (GA) to solve real-sized problems was developed and tested. The results of the study indicate that the proposed model with the linear double-row layout provides more reliable results than the single-row layout, and the GA achieves acceptable solutions in a more reasonable time period compared to the traditional solution method. [Submitted 30 December 2019; Accepted 28 July 2020]

Benders decomposition approach to solve a Bi-objective cell formation in dynamic conditions

Cellular Manufacturing System (CMS) is an alternative to production systems based on process and product layout that combines the advantage of high throughput rate of flow shop system with the flexibility of job shop layout. CMS design has four steps: 1) Cell formation: grouping the parts with similar geometric design features and processing requirements in part families to use similarities to build, manufacture and assemble machines in machine lines and cells, 2) group layout: placing machines in each cell that includes intra-cell and inter-cell layout, 3) group scheduling: part family scheduling, and 4) source allocation: allocation of tools and primary resources and human force. Dynamic CMS (DCMS) can be divided into smaller periods, each of which is different from the previous one as these periods have various demands and compositions. The study presented a mixed-integer programming model for DCMS. The model considered some significant real-world conditions. In this model, the first objective function tries to minimize the costs related to the machines purchase cost, machines variable cost, the cost of movement workers, the cost of cell formation, the cost of inter-cell and intra-cell part movement, the cost of overtime, and the cost of reconfiguration.

قياس مدى تطبيق عناصر استراتيجية التصنيع الرشيق في مصانع الثوب الرجالي في المملكة العربية السعودية

Lean manufacturing strategy refers to the work system followed during production processes within the industrial facility, with the aim of reducing waste, time and cost used in implementing the product in order to reach the consumer’s satisfaction by improving the level of product quality. The aim of the research is to: measure the extent to which the elements of the lean manufacturing strategy are applied in men's clothing factories in the Kingdom of Saudi Arabia. The research used: the descriptive approach through the analytical study of the factory’s and the product’s components. The research found: There is a shortcoming in the application of the elements of the Lean manufacturing strategy in men's clothing factories (worksite organization (5S), comprehensive production maintenance, continuous improvement, preparation / rapid change, cellular manufacturing), which leads to a large waste in the production time process, while the component (production on time) found superiority and distinction in its application. The research recommended: Overcoming the difficulties facing the application of each element of the Lean Manufacturing strategy, to take advantage of its multiple features. Continuing to strive for excellence in the application of the production element on time and in a better manner, taking into account the promotion of eliminating waste and loss in all its forms and in all stages of production processes.

Operational reliability and quality loss of diversely configurated manufacturing cells with heterogeneous feedstocks

Machine reliability in cellular manufacturing is a challenging engineering problem in the formation and design of manufacturing cells. The heterogeneity of feedstock quality is also common in manufacturing industry. However, so far, no work has been done to investigate the performance of diversely configurated manufacturing cells under the heterogeneous feedstocks. In this paper, considering the actual engineering condition, the uniformly random arrival and the clustered arrival of low-quality feedstocks are proposed and modeled by the homogeneous Poisson process and Hawkes process, respectively. Also, to study the mixed reliability of a machine under the impact of heterogeneous feedstocks, a mixed failure-rate model is constructed by the mixture of exponential and Weibull distributions, and the processing quality is modeled by a non-homogeneous Poisson process with a dynamic intensity function. Then, we achieve a contrastive analysis for operational reliability and quality loss of manufacturing cells with basic serial and parallel configurations under the impact of heterogeneous feedstocks. At last, the designed simulation illustrates the effectiveness of our proposed models, and some results are concluded to provide some guidelines for the design of manufacturing cells.

Determination of the Error in Positioning Body Parts for Their Automatic Installation in Cellular Manufacturing Systems

Determination of the Error in Positioning Body Parts for Their Automatic Installation in Cellular Manufacturing Systems

Establishment and validation of in-house cryopreserved CAR/TCR-T cell flow cytometry quality control

BackgroundChimeric antigen receptor (CAR) or T-cell receptor (TCR) engineered T-cell therapy has recently emerged as a promising adoptive immunotherapy approach for the treatment of hematologic malignancies and solid tumors. Multiparametric flow cytometry-based assays play a critical role in monitoring cellular manufacturing steps. Since manufacturing CAR/TCR T-cell products must be in compliance with current good manufacturing practices (cGMP), a standard or quality control for flow cytometry assays should be used to ensure the accuracy of flow cytometry results, but none is currently commercially available. Therefore, we established a procedure to generate an in-house cryopreserved CAR/TCR T-cell products for use as a flow cytometry quality control and validated their use.MethodsTwo CAR T-cell products: CD19/CD22 bispecific CAR T-cells and FGFR4 CAR T-cells and one TCR-engineered T-cell product: KK-LC-1 TCR T-cells were manufactured in Center for Cellular Engineering (CCE), NIH Clinical Center. The products were divided in aliquots, cryopreserved and stored in the liquid nitrogen. The cryopreserved flow cytometry quality controls were tested in flow cytometry assays which measured post-thaw viability, CD3, CD4 and CD8 frequencies as well as the transduction efficiency and vector identity. The long-term stability and shelf-life of cryopreserved quality control cells were evaluated. In addition, the sensitivity as well as the precision assay were also assessed on the cryopreserved quality control cells.ResultsAfter thawing, the viability of the cryopreserved CAR/TCR T-cell controls was found to be greater than 50%. The expression of transduction efficiency and vector identity markers by the cryopreserved control cells were stable for at least 1 year; with post-thaw values falling within ± 20% range of the values measured at time of cryopreservation. After thawing and storage at room temperature, the stability of these cryopreserved cells lasted at least 6 h. In addition, our cryopreserved CAR/TCR-T cell quality controls showed a strong correlation between transduction efficiency expression and dilution factors. Furthermore, the results of flow cytometric analysis of the cryopreserved cells among different laboratory technicians and different flow cytometry instruments were comparable, highlighting the reproducibility and reliability of these quality control cells.ConclusionWe developed and validated a feasible and reliable procedure to establish a bank of cryopreserved CAR/TCR T-cells for use as flow cytometry quality controls, which can serve as a quality control standard for in-process and lot-release testing of CAR/TCR T-cell products.

A Meta-Heuristic Approach Based on the Genetic and Greedy Algorithms to Solving Flexible Job-Shop Scheduling Problem

In today’s competitive business world, manufacturers need to accommodate customer demands with appropriate scheduling. This requires efficient manufacturing chain scheduling. One of the most important problems that has always been considered in the manufacturing and job-shop industries is offering various products according to the needs of customers in different periods of time, within the shortest possible time and with rock-bottom cost. Job-Shop Scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in various aspects of construction and design. In addition, these systems are identified as Cellular Manufacturing Systems (CMS). Today, applying CMS and the use of its benefits have been very important as a possible way to increase the speed of the organization’s response to rapid market changes. In this paper, a meta-heuristic method based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated in a greedy algorithm and several elitist operators are used to improve the solutions. The greedy algorithm which is used to improve the generation of the initial population prioritizes the cells and the job in each cell, and thus offers quality solutions. The proposed algorithm is tested over P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality and run-time criteria were used in a multi-objective function. The results of simulation indicate better performance of the proposed method compared to NRGA and NSGA-II methods.

Developing a bi-objective schedule for an online cellular manufacturing system in an MTO environment

Developing a bi-objective schedule for an online cellular manufacturing system in an MTO environment

Soft Computing Methodology to Optimize the Integrated Dynamic Models of Cellular Manufacturing Systems in a Robust Environment

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

Making an integrated decision in a three-stage supply chain along with cellular manufacturing under uncertain environments: A queueing-based analysis

Today’s complicated business environment has underscored the importance of integrated decision-making in supply chains. In this paper, a novel mixed-integer nonlinear mathematical model is proposed to integrate cellular manufacturing systems into a three-stage supply chain to deal with customers’ changing demands, which has been little explored in the literature. This model determines the types of vehicles to transport raw materials and final parts, the suppliers to procure, the priorities of parts to be processed, and the cell formation to configure work centers. In addition, queueing theory is used to formulate the uncertainties in demands, processing times, and transportation times in the model more realistically. A linearization method is employed to facilitate the tractability of the model. A genetic algorithm is also developed to deal with the NP-hardness of the problem. Numerous instances are used to validate the effectiveness of the modeling and the efficiency of solution procedures. Finally, a sensitivity analysis and a real case study are discussed to provide important management insights and evaluate the applicability of the proposed model.

Heuristic-based approach to solve layout design and workers’ assignment problem in the cellular manufacturing system

ABSTRACT The design process of a cellular manufacturing system (CMS) involves well-known NP-hard problems. Being an NP-hard, it is difficult to address these CMS-related problems optimally within a reasonable time. Therefore, this paper proposes a novel heuristic-based two-stage approach to tackle the CMS-related problems. In the first stage, a mathematical model is developed to concurrently solve cell formation and workers’ assignment problems (CFWAP). The worker’s selection is determined by their performance in operating a machine for a specific part, measured by the defect rate resulting from assigning the worker to a machine. The model’s objective is to minimize the weighted sum of the total inter-cell, intra-cell movements, operation, and defect costs. In the second stage, based on the output of the mathematical model in the first stage, a heuristic algorithm is developed to solve the problem related to the layout design of the cellular manufacturing system (LDCMS). The actual dimensions of machines and the aisle distances between them are considered in LDCMS to find out the best possible allocation of the machines and cells within the entire system. A comparison is conducted between numerical examples from the literature to show the efficiency and the validation of this two-stage heuristic approach. The results show that the proposed method can optimally tackle the aforementioned CMS-related problems within a reasonable time. To the best of our knowledge, this is the first study that integrates the CFWAP with LDCMS, considering all these practical and manufacturing features. Finally, it should be noted that the proposed approach has been applied as a real case study for a central workshop on die manufacturing for a food stove company in Egypt. The results depict a significant reduction in the company’s total associated costs by achieving up to 23% savings from applying the proposed method.

Modeling the Scheduling Problem in Cellular Manufacturing Systems Using Genetic Algorithm as an Efficient Meta-Heuristic Approach

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.

A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

Evolutionary algorithms for multi-objective flexible job shop cell scheduling

The multi-objective flexible job shop scheduling in a cellular manufacturing environment is a challenging real-world problem. This recently introduced scheduling problem variant considers exceptional parts, intercellular moves, intercellular transportation times, sequence-dependent family setup times, and recirculation requiring minimization of makespan and total tardiness, simultaneously. A previous study shows that the exact solver based on mixed-integer nonlinear programming model fails to find an optimal solution to each of the ‘medium’ to ‘large’ size instances considering even the simplified version of the problem. In this study, we present evolutionary algorithms for solving that bi-objective problem and apply genetic and memetic algorithms that use three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all evolutionary algorithms with various configurations is investigated across forty-three benchmark instances from ‘small’ to ‘large’ size including a large real-world problem instance. The experimental results show that the transgenerational memetic algorithm using weighted sum outperforms the rest producing the best-known results for almost all bi-objective flexible job shop cell scheduling instances, in overall.

HUMAN COGNITION INSPIRED PROCEDURES FOR PART FAMILY FORMATION BASED ON NOVEL INSPECTION BASED CLUSTERING APPROACH

Human cognition based procedures are promising approaches for solving different kind of problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to part machine grouping, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics. Keywords: Cellular manufacturing systems; part family formation; human cognition; inspection-based clustering.

Group Technology for Optimizing Manufacturing Facility Layout

In recent years, the process of cellular manufacturing and group technology have received a lot of attention and popularity in many developed countries. By applying Group Technology (GT), many benefits of flow-line production can be attained in a batch production system. GT improves material handling significantly by reducing material flow time, distance, and setup times. In this paper, an earnest investigative attempt was made to provide valuable information regarding the use of Group Technology by applying to a real world jobshop system. The proposed GT model has the flexibility of choosing the number of cells required, which is very useful in examining different manufacturing cell configurations; or in case the workshop or factory prefers a certain number of work cells. The GT model results were found satisfactory and superior to other techniques in some cases.