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Reports on a new methodology for formation of virtual (“extended”) machining cells using generic capability patterns termed “resource elements”. Resource elements are used to uniquely describe the processing requirements of the component mix and dynamically match them to the processing capabilities of the machining shop. The virtual cell formation methodology is based on four steps: component requirement analysis and generation of processing alternatives; definition of virtual cell capability boundaries; machine tool selection; and system evaluation. The proposed methodology facilitates the dynamic formation of virtual manufacturing structures by providing accurate assessment of the component processing requirements and their matching with the available capabilities of the existing manufacturing facilities.

The grouping of parts and machines for design of cellular manufacturing systems is carried out by clustering analysis. Two major drawbacks of some clustering algorithms have been identified in handling bottleneck machines for forming machine cells. These drawbacks include solution inconsistency and possible misclustering which result in unnecessary bottleneck machines required. Presents a more robust clustering algorithm to overcome these drawbacks. The algorithm consists of four stages: selection of initial cluster centres; cluster‐seeking analysis; eliminating unnecessary bottleneck machines; and new parts assignments. The decision functions based on the formed machine cells are defined to assign new parts to the machine cells. The algorithm is capable of selecting an ideal set of initial cluster centres, and minimizing the number of bottleneck machines required for forming the desired number of machine cells. It can also provide alternative design of machine cells to accommodate the existing production environment.

Among the many accepted clustering techniques, the fuzzy clustering approaches have been developed over the last decades. These approaches have been applied to many areas in manufacturing systems. In this paper, a fuzzy clustering approach is proposed for selecting machine cells and part families in cellular manufacturing systems. This fuzzy approach offers a special advantage over existing clustering approaches as it presents the degree of membership of the machine or part associated with each machine cell or part family allowing users flexibility in formulating machine cells and part families. The proposed algorithm is extended and validated using numerical examples to demonstrate its application in cellular manufacturing.

The decomposition of production facilities into efficient cells is one of the areas attracting increasing research attention due to the performance benefits which cellular manufacturing offers. One of the problems associated with cell formation is the restrictive character of the existing task formalization models resulting in most cases from the use of single machine tool routeings in representing the component requirements. In contrast, the industrial reality provides a more complex picture with multiple choice of processing routeings in terms of available machine alternatives which need to be considered in order to achieve an “optimum” cellular decomposition of manufacturing facilities. Presents a facility decomposition approach based on multiple choice of processing alternatives for each component. The decision making is based on a generic description of the component processing routeings using unique machine capability patterns ‐ “resource elements”. Manufacturing cells are formed using concurrent fuzzy clustering methodology and a validation procedure for selection of the “optimum” facility partition.

The purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.

Similarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.

Using similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.

The proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.

This paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

Describes a company‐based PhD project into the use of automated guided vehicles in a small‐batch manufacturing environment. The project led to a balanced‐cell methodology to facilitate the use of guided vehicles in a difficult environment. The methodology itself was found to provide benefits for material flow. Having formulated the above approach, a theoretical model is presented, analysing the operational effects of improved workflow. The above theoretical analysis showed the potential benefits of balanced cells on the factory floor, and these were confirmed by a simulation study. This being so, a DCF analysis showed that balanced cells enabled the economic use of guided vehicle systems in multi‐product batch manufacture, by transforming an AGV project from a negative to a positive net present value. An analysis of the wider effects of cellular manufacture enabled the value of the investment to be increased.

This work addresses the problem of specification of cellular manufacturing systems (CMS), identifying the machine cells and the respective part families, as well as considering some constraints usually met during the CMS’ installation. For this, we developed a heuristic procedure consisting of two phases, where in the first phase, a version of the breadth‐first search algorithm is used to allocate parts to the machines, in a way not to violate the processing capacity of the machines; and in the second phase, a genetic algorithm is used to identify the machine cells and the respective part families.

The aim of this paper is to illustrate a solution that can be used to reduce the severity of breakdowns and improve performances in the cellular manufacturing (CM) system with unreliable machines.

The performance of CM system is conditioned by disruptive events, such as the failure of machines, which randomly occurs and penalizes the performance of the cells, seriously disturbing the smooth working of the factory. To overcome the problem caused by the breakdowns, the authors develop a solution, based on the principle of virtual cell and the notion of intercellular transfer that can improve the availability of the system. In this context, the use an analytical method based on Markov chains to model the availability of the cell. The results are validated using simulation.

The proposed solution in this paper confirmed that it is possible to reduce the severity of breakdowns in the CM system and improve the availability of the cells through an intercellular transfer created at the time of a breakdown. Simulation allowed a validation of the analytical model and showed the contribution of the suggested solution.

The developed approach studies the performance of the production cells formed by unreliable machines. It uses the notion of the intercellular transfer to improve the availability of the cells.

The design of a cellular manufacturing system requires that a machine population be partitioned into machine groups called manufacturing cells. A new graph partitioning heuristic is proposed to solve the manufacturing cell formation problem (MCFP). In the proposed heuristic, The MCFP is represented by a graph whose node set represents the machine cluster and edge set represents the machine‐pair association weights. A graph partitioning approach is used to form the manufacturing cells. This approach offers improved design flexibility by allowing a variety of design parameters to be controlled during cell formation. The effectiveness of the heuristic is demonstrated by comparing it to two MCFP published solution methods using several problems from the literature.

Compares two approaches for designing production line cellular manufacturing systems, or “focused factories”. Production line manufacturing cells are dedicated to producing a set of similar products with machines that are arranged in a serial production line within the cell. Approach A requires the processing of batches of products in only one manufacturing cell, while approach B allows batches of product to be split and made simultaneously in different cells. Both design approaches consist of three stages. The first stage determines the best part routings among alternate routings to minimize the operating cost. At the second stage, a specific number of cells is obtained by using an ART1 neural network‐based cell formation module in approach A and a fuzzy rank order clustering (fuzzy ROC) module in approach B. At the third stage, production sequence is considered to find the best layout with lowest material handling cost. An example demonstrates that both approaches are effective in designing production line cellular manufacturing systems. Approach B gives a lower operating cost but a higher material handling cost than approach A. Both approaches should be analysed and compared, since the best approach depends on the operating and material handling costs for the application.