Performance of manufacturing cells for group technology: a parametric analysis

Once the cellular manufacturing system is designed, scheduling of jobs is essential for the day-to-day production in the machine cells. Scheduling in cellular manufacturing system is generally complicated. In this paper, a methodology has been proposed for prioritizing the parts, as well as preparing the total schedules in a cellular manufacturing system. It takes into account, the processing sequences of the jobs, processing and setup times and due dates.

This paper uses set theory to analyze cellular manufacturing systems. We develop a structural architecture to investigate the previous literature. We divide all research problems into three types — what, why, and how. We advocate studying these three types of problems from two managerial perspectives — philosophy and science. To fully and deeply understand cellular manufacturing, both three-problems and two-perspectives are important. We use the developed architecture to review cellular manufacturing literature and point out the weakness in the previous studies. This review motivates the research of this paper. We use simple but rigorous set theory to analyze two what-problems. Underlying concepts, such as part family, machine cell, cellular manufacturing system, bottleneck machine are defined. Following these definitions, we deduce sufficient and necessary conditions for perfectly partitioned cellular manufacturing systems. Two sufficient and necessary conditions of perfect partition have been discovered — prior perfect partition theorems check the potential independent manufacturing cells within an arbitrary manufacturing system; in contrast, posterior perfect partition theorem verifies a perfectly partitioned cellular manufacturing system. To our knowledge, we are among the first to apply rigorous mathematical models to these two types of what-problems for cellular manufacturing. We try to provide mathematical vocabularies to discuss common problems inherent in the design of cellular manufacturing systems.

The three-machine flowshop scheduling problem to minimize total completion time is studied where setup times are treated as separate from processing times. Setup and processing times of all jobs on all machines are unknown variables before the actual occurrence of these times. The lower and upper bounds for setup and processing times of each job on each machine is the only information that is available. In such a scheduling environment, there may not exist a unique schedule that remains optimal for all possible realizations of setup and processing times. Therefore, it is desired to obtain a set of dominating schedules (which dominate all other schedules) if possible. The objective for such a scheduling environment is to reduce the size of dominating schedule set. We obtain global and local dominance relations for a three-machine flowshop scheduling problem. Furthermore, we illustrate the use of dominance relations by numerical examples and conduct computational experiments on randomly generated problems to measure the effectiveness of the developed dominance relations. The computational experiments show that the developed dominance relations are quite helpful in reducing the size of dominating schedules.

Performance of hybrid cellular manufacturing systems: A computer simulation investigation

The formation of machine–part families is an important task in the design of cellular and flexible manufacturing systems. The formation results in the creation of many benefits for manufacturing systems. Among the many methods utilised in machine–cells formation, the similarity coefficient method (SCM) is most widely used. When SCM is used, the rearrangement of machine and part components is required to form machine–part families. This process of rearrangement has been considered as being subjective and difficult, may result in improper assignments to parts families, resulting in a negation of benefits promised. This paper presents an effective algorithm to identify part–families and bottleneck parts, given machine groupings, rather than addressing the machine grouping problem in general.

Ensuring correct functionality is the basic objective of any digital design and this can be done only if the circuit obeys all the timing constraints. Static Timing Analysis is the procedure performed to calculate the setup and hold time of any digital circuit and to check for any setup and hold time violations. This paper presents the methodology to perform setup and hold time analysis for different designs of Master Slave D Flip-Flop using 18 nm FinFET technology. FinFET technology was chosen since it is the prevalent technology in the industry. Implementation of D Flip-Flop design, suitable test circuit and timing analysis is performed on the Cadence virtuoso analog design environment tool. Using appropriate excitations, parametric analysis is carried out to obtain the setup and hold time values for various designs of D Flip-Flop including the proposed design. From the results, it was observed that the setup time and hold time varied with different clock edge rates. For the edge rates of 0.01, 0.05, and 0.1 ns, it was observed that the proposed design gave setup times of 54.9, 61.2, and 69.8 ps and hold times of −30.7, −43.6 and, −48.8 ps, respectively. On comparing all the results, it was observed that the GDI MUX and pass transistor-based designs gave a good trade-off between transistor count, setup time and hold time. For the proposed design, although the setup times were slightly on the higher side, the hold time values were more negative which is always preferred.

In the competitive shoe manufacturing industry, efficient production scheduling often struggles to balance minimizing makespan (MS) and reducing the number of cells, leading to increased complexity. This study introduces a biobjective optimization model to minimize the MS and the number of cells in a cellular manufacturing (CM) system. The model includes three phases: (1) a capacity phase calculates the required number of cells to meet the production demand within a suitable production rate; (2) a scheduling phase calculates the start and finish times for each product within a machine; (3) an optimization phase uses genetic algorithm (GA) for generating a set of optimal solutions considering the workforce distribution and product sequencing. The performance of the developed model was evaluated using a case study discussed in the literature to demonstrate its capabilities and highlight its superiority over similar models. These results showed a significant reduction of 0.4% in MS while maintaining the same number of cells compared to other models. A sensitivity analysis is conducted by varying the crossover and mutation probabilities. The findings indicate that the model achieves the most stable and consistent performance for paired crossover–mutation settings with crossover probabilities between 70% and 90% (corresponding to mutation probabilities between 30% and 10%), resulting in the lowest average MS and the most balanced cell utilization. The capabilities of the developed model provide production managers with reliable insights for identifying the production requirements of the shoe manufacturing company. The algorithm was also benchmarked on 12 published instances using 30 independent runs per instance to assess solution quality and stability.

A number of companies have reaped the benefits offered by cellular manufacturing systems (CMSs) (Wemmerlov and Hyer, 1986). Typical benefits include: set-up time reduction, improved machine utilization, an increase in production rate and employee morale, a decrease in labor costs and traffic congestion and better control of the manufacturing system. Cellular manufacturing (CM) involves grouping of machines based on the parts they manufacture. The grouping of machines and parts in a CMS is briefly explained here by considering a simple example. Figure 9.1 shows four part routings. The first row indicates that part 1 is routed through machines A, C and E in succession. Figure 9.2 shows the arrangement of the machines referred to in Fig. 9.1 in a job shop environment. Figure 9.3 shows the arrangement of the same machines in ‘cells’. Notice that each cell is dedicated to production of a set of parts. The main idea behind grouping ‘like’ machines into cells is to minimize intercellular part movement. After grouping is done, the next logical step is to develop a layout for the cells and machines within each cell so that both intercellular and intracellular material handling cost is minimized. This step, however, is often ignored.

Under stochastic demand setting, cellular manufacturing system is planned and assessed according to its profitability. The approach in this paper is composed of five stages. First, the cellular system is studied by defining processing time information and component demand according to the matrix of the part-machine and the demand data. The cellular system is then configured or planned through the following steps: the determination of means, variance and standard deviation of the capacity specifications for parts of the network, the determination of the probabilities of demand coverage for the part network and the determination of the intended usage of cells. Secondly, the estimated revenues from the cellular architecture are calculated second, the overall production costs of part family open cells are estimated taking into account various scenarios of potential unit shortages. Fourthly, the expected profits generated by open cells are decided, and finally the decision is made on the optimal design that yields the greatest profits with its related likelihood of demand coverage. The results show that while five manufacturing cells satisfy the expected part family demand, three manufacturing cells produce the system's highest income.

In today’s environment of escalating competition, companies are adapting their management and production strategies, and product diversity is rapidly increasing. Companies require cellular manufacturing systems to produce products with high diversity in a short amount of time, ensuring the desired quality and meeting customer expectations. Cellular manufacturing systems, which have a more flexible structure compared to traditional production systems, are a good and effective solution for managers. Cellular manufacturing is an approach that aims to produce products with varying diversity in the shortest possible time and at the lowest cost, targeting an increase in efficiency. In this study, a cell manufacturing system proposal is made and cell formation is carried out to increase efficiency and effectiveness in a company that manufactures industrial refrigeration cabinets. A productivity-based 0-1 integer mathematical programming model is prepared that facilitates the simultaneous grouping of part and machine families in cell formation. In addition to the intracellular and intercellular transportation costs found in productivity-based models in the literature, labor costs, maintenance costs, the depreciation costs of the machines used in the cells, and the waiting costs of the machines are also added to the prepared model. The model is solved with the help of the GAMS 23.5.1 software package, creating part families and machine groups. Group efficiency values are measured, and the current and proposed situations are compared.

A capacity constrained mathematical programming model for cellular manufacturing with exceptional elements

Globalization and rapid technological changes have led to increased customer requirements and an intensified competition. To remain cost-efficient, manufacturing companies move from traditional manufacturing systems towards flexible manufacturing systems. Reconfigurable manufacturing systems have proven to be an effective way of adapting to the rapidly changing market conditions. Although, an increasing interest in this topic is visible in academics and industrial practice, the research is still limited. From an industrial point of view, a fast and easy adaptable solution for managing such a system dynamically is required. Based on an optimization model, a genetic algorithm for the dynamic management of cellular reconfigurable manufacturing systems was developed. A case study shows the effects of varying the selection method, the crossover operator, and the values for the occurrence of crossover and mutation processes. The application of the genetic algorithm resulted in an improvement of around 3% compared to the best solution of the initial population. Random selection showed the best results in the respective case. Nevertheless, it can be assumed that this selection method is outperformed by others as the number of generations increases.KeywordsReconfigurable manufacturingGenetic algorithmSelectionCrossoverMutationParameter variation

In this paper, Virtual Cellular Manufacturing (VCM), an alternative approach to implementing cellular manufacturing, is investigated. VCM combines the setup efficiency typically obtained by Group Technology (GT) cellular manufacturing (CM) systems with the routing flexibility of a job shop. Unlike traditional CM systems in which the shop is physically designed as a series of cells, family-based scheduling criteria are used to form logical cells within a shop using a process layout. The result is the formation of temporary, virtual cells as opposed to the more traditional, permanent, physical cells present in GT systems. Virtual cells allow the shop to be more responsive to changes in demand and workload patterns. Production using VCM is compared to production using traditional cellular and job shop approaches. Results indicate that VCM yields significantly better flow time and due date performance over a wide range of common operating conditions, as well as being more robust to demand variability.

Cellular Manufacturing Systems (CMS) have been widely considered as the most efficient manufacturing systems in the case of medium variety and medium volume of production. The main advantage of CMS lies in the effective grouping of parts into families and machines in to corresponding groups as it results in minimizing the number of intercellular moves. Over the years, a number of efficient approaches have been developed by researchers to handle the Cell Formation Problem (CFP). Among these, a large number of approaches consist of Artificial Intelligence (AI) based techniques. The main advantage of such approaches is their ability to handle the CFP effectively both in terms of accuracy and computational effort. Following the same trend an evolutionary algorithm has been developed during this research by combining Standard Genetic Algorithm with a very effective Local Search Heuristic (LSH). The results show that it is efficient both in terms accuracy and speed of convergence (CPU time).

Design of layouts for Cellular Manufacturing Systems (CMSs) has not received attention by researchers as much as cell formation in cellular manufacturing. In this paper a mathematical model, formulated as a multicriteria Quadratic Assignment Problem (QAP) is proposed for the design of layouts for CMSs with the objective of minimising the material handling cost (quantitative) and maximising the closeness rating (qualitative). A Genetic Algorithm (GA)-based tool called FUGEN, which makes use of crossover, mutation (inversion) and direct entry (elitism) with embedded fuzzy logic and Analytic Hierarchy Process (AHP) models, is developed for the design of layouts for CMSs. An attempt is made to make the model practical by considering the production volume, transfer batch size and operational sequence of parts. The proposed tool is validated using an example from the literature.