Cellular manufacturing system design using grouping efficacy-based genetic algorithm

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.

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 design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

In this paper, a multi-objective integer programming model is constructed for the design of cellular manufacturing systems with independent cells. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. There are some features that make the proposed algorithm different from other algorithms used in the design of cellular manufacturing systems. These include: (1) a systematic uniform design-based technique, used to determine the search directions, and (2) searching the solution space in multiple directions instead of single direction. Four problems are selected from the literature to evaluate the performance of the proposed approach. The results validate the effectiveness of the proposed method in designing the manufacturing cells.

A cellular manufacturing system is defined as a system which has one or more manufacturing cells. A manufacturing cell is a collection of dissimilar machines dedicated to a collection of similar parts. An important problem with the design of cellular manufacturing systems is how reasonable manufacturing cells can be constructed. This problem is considered as an application of group technology, that is, a problem of part family definition and machine assignment. A concept called group efficiency has been proposed as a quantitative criterion for a manufacturing cell formation. But, this is not a measure for a unit cell which is formed with a part family and a machine group. Thus, a new measure, that is, a "coefficient of bond degree", has been introduced for a unit cell. Manufacturing cells can be compared using a "bond degree" defined for each cell. Also, a new cell formation method based on the bond degree has been proposed, and a numerical example has been shown.

A new, efficient clustering method for solving the cellular manufacturing problem is presented in this paper. The method uses the part-machine incidence matrix of the manufacturing system to form machine cells, each of which processes a family of parts. By doing so, the system is decomposed into smaller semi-independent subsystems that are managed more effectively improving overall performance. The proposed method uses Self Organizing Maps (SOMs), a class of unsupervised learning neural networks, to perform direct clustering of machines into cells, without first resorting to grouping parts into families as done by previous approaches. In addition, Latent Semantic Indexing (LSI) is employed to significantly reduce the complexity of the problem resulting in more effective training of the network, significantly improved computational efficiency, and, in many cases, improved solution quality. The robustness of the method and its computational efficiency has been investigated with respect to the dimension of the problem and the degree of dimensionality reduction. The effectiveness of grouping has been evaluated by comparing the results obtained with those of the k-means classical clustering algorithm.

This paper investigates the problem of designing cellular manufacturing systems with multi-period production planning, dynamic system reconfiguration, operation sequence, duplicate machines, machine capacity and machine procurement. An important aspect of this problem is the introduction of routing flexibility in the system by the formation of alternate contingency process routings in addition to alternate main process routings for all part types. Contingency routings serve as backups so as to effectively address the reality of part process routing disruptions (in the main routings) owing to machine breakdowns and allow the cellular manufacturing system to operate in a continuous manner even in the event of such breakdowns. The paper also provides in-depth discussions on the trade-off between the increased flexibility obtained versus the additional cost to be incurred through the formation of contingency routings for all parts. Some sensitivity analysis is also performed on some of the model parameters. The problem is modelled and solved through a comprehensive mixed integer programming formulation. Computational results presented by solving some numerical examples show that the routing and process flexibilities can be incorporated within the cellular manufacturing system design without significant increase in the system cost.

A vibration damping optimization algorithm for the integrated problem of cell formation, cellular scheduling, and intercellular layout

Design of cellular manufacturing systems—An industrial case study

This paper is concerned with machine-cell and part-family formation for the design of cellular manufacturing systems. The paper has two distinguished purposes. The first purpose of the paper is to introduce a modified p-median model, an integer linear programming formulation, for the machine-cell or partfamily formation problem. Compared with the original p-median model, the modified model has additional desirable features. The modified model allows the control of the size of machine cells or part families by introducing an upper bound on the maximal number of machines per cell or maximal number of parts per family. The modification also results in a substantial reduction of the number of constraints in the model formulation. The second purpose of this paper is to present various computational results in a comparative study for machine-cell and part-family formation based on the original and modified p-median models using three different definitions of similarity coefficients. Guidelines are given to choose design parameters, definition of similarity coefficients, and appropriate solution sequence of machine-cell and part-family formation.

The current research seeks to present a proposed model for the design of a dynamic cellular manufacturing system in the Light Industries Company/Ashtar in Baghdad, one of the Iraqi industrial companies. With the aim of generating an industrial environment capable of accommodating rapid changes in the market to meet the changing and diverse needs and desires of the customer at the lowest possible cost, at the right time and the required quality. To achieve the current research relied on the methods of artificial intelligence, employing them and benefiting from them in the industrial field, especially the genetic algorithm. Accordingly, the current research included a theoretical framework for the dynamic cellular manufacturing system and the genetic algorithm, in light of what was mentioned in the Arab and foreign sources related to them. The research also adopted a field framework to design this system in the researched company using the genetic algorithm, and the researchers chose the freezers and models labs as a field for study, and in light of the results of the programming language program (MATLAB). It was reached to build a proposed model that reflects the virtual reality of this system. On the basis of that, the research reached a set of conclusions, the most important of which is the genetic algorithm that contributed significantly and effectively to finding the optimal cellular path for the dynamic cellular manufacturing system and reducing both the total time and the total cost of production. In light of this a set of proposals were presented showing the importance of shifting from the existing cellular manufacturing system in the field of study to the dynamic cellular manufacturing system.

Cell formation and machine layout in cellular manufacturing systems (CMs) design are considered as a crucial, yet hard and complex decision process. Owing to the nondeterministic polynomial time (NP) and combinatorial class of this problem, this paper presents an innovative heuristic approach to re‐arrange machines enabling the minimisation of inter/intra‐ cellular movements as well as the cost of material handling between machines, therefore increasing group efficiency and efficacy. The heuristic approach, which is based on group technology, genetic algorithms, and desirability function, determines the optimal solution for flexible cell formation and machine layout within each cell. Flexibility refers to an explicit improvement using the desirability function to modify cell design by altering the ratio data; that is, the weight factor to meet demand flexibility. Specifically, the desirable function proposed here to provide the optimal setting of the weighting factor as a key factor which enables CMs design the flexibility to control the cell size. Promised results were obtained when the proposed approach was applied to a case study. Practical implications and recommendations are provided for use by decision makers in the design of CMs.

A new concept is presented in this paper of quasi-dynamic cell formation for the design of a cellular manufacturing system, based on analysing the fact that static and dynamic cell formation could not reflect the real situation of a modern cellular manufacturing system. Further, workforce resources are integrated into quasi-dynamic cell formation and thus a quasi-dynamic dual-resource cell-formation problem is proposed. For solving this problem, this paper first establishes a non-linear mixed integer programming model, where inter-cell and intra-cell material cost, machine relocation cost, worker operation time, loss in batch quality and worker salary are to be minimised. Then, a multi-objective GA is developed to solve this model. Finally, a real life case study is conducted to validate the proposed model and algorithm. The actual operation results show that the case enterprise significantly decreases its material handling cost and workforce number and obviously increases its product quality after carrying out the obtained scheme.

This paper deals with the cell formation problem that is the first step in designing cellular manufacturing system (CMS). The previous methods that were based on the array-based clustering for solving the cell formation problem have used the part-machine incidence matrix (PMIM) which was indicated the set of machines for processing each part. The main problem of these methods is the grouping parts and machines regardless of production volume, operational sequences, production cost, inventory and other production system's limitations. In this paper we intend to implement the multiple attribute decision making (MADM) concepts for covering the cause of drawback in the previous array-based clustering methods by considering two definitions of the arrays in PMIM. Four methods are presented that two methods are related to the binary arrays in PMIM and other two methods are considered the operational time as well as the operational sequencing in the definition of the arrays in PMIM. The results of the presented methods that are compared with a well-known approach are also reported.

International Engineering Consortium predicted that cellular manufacturing would become the main production model of manufacturing enterprises in the future. In recent years, studies both at home and abroad were primarily around cellular manufacturing system design and equipment layout, while research on cellular manufacturing management pattern was relatively small. Based on elaborating the basic concepts and organizational forms of cellular manufacturing, this paper gives autonomous management models of cellular manufacturing, and proposes optimization management method, so as to expand production management theory and provide reference to cellular manufacturing enterprises.