A HIERARCHICAL DIVISIVE CLUSTERING METHOD FOR MACHINE-COMPONENT GROUPING PROBLEMS

This paper proposes a new approach based on graph theory for the machine-component grouping problem in a cellular manufacturing system. The objective is to form components into part families such that the degree of interrelations is high among components within the same part family and low between components of different part families.

Since finding an optimal solution through total enumeration is prohibitive in terms of time and efforts even for problems with a moderate number of components, a heuristic algorithm is proposed. The algorithm is hierarchical and divisive in nature and illustrated with numerical examples.     

Production data based similarity coefficient for machine-component grouping decisions in the design of a cellular manufacturing system

This paper presents a similarity coefficient based approach to the problem of machine-component grouping. The proposed method incorporates relevant production data such as part type production volume, routing sequence and unit operation time in the early stages of grouping decisions for cellular manufacturing. The algorithm also suggests a methodology for evaluating alternative solutions from different algorithms on a quantitative basis using a modified version of an existing coefficient. The modified quantitative measure is a comprehensive indicator for the goodness of a grouping solution. The algorithm then identifies bottleneck machines and corresponding cell candidates for their duplication using percentage utilization in each cell as a criterion. Finally, additional constraints can be applied to determine the best grouping solution among alternative solutions generated by the algorithm. A software package has been developed to verify the implementation.     

A similarity coefficient measure and machine-parts grouping in cellular manufacturing systems

For more than three decades, similarity coefficient measures one of the important tools for solving group technology problems have gained the attention of the research community in cellular manufacturing systems. A new similarity coefficient measure that uses a set of important characteristic properties for grouping is developed here for use as an intermediate tool to form cohesive cells. A mathematical model that uses this similarity coefficient for optimally solving the cell-formation problems in cellular manufacturing is developed. A heuristic procedure that improves the optimal methodology in term of solution capability of the large instances is devised for an efficient solution. Both the optimal methodology and the heuristic are applied to some well-known problems from literature to compare the grouping efficiencies. The similarity coefficient and the solution methodologies developed are able to solve the cell formation problems efficiently.     

The threshold value of a quality index for formation of cellular manufacturing systems

The superiority of cellular manufacturing to job shop manufacturing has been questioned by a number of simulation studies. The initial structure of the machine-part matrix seems to play an important role in the failure of cellular manufacturing systems in these studies. In this paper a grouping measure called ‘quality index— QI’ will be used to evaluate the relationship between the quality of a machine-part matrix and the performance of the corresponding cellular manufacturing system. A simulation study will be conducted to demonstrate how the procedure can be used to determine the threshold value for QI beyond which the cellular manufacturing system outperforms the corresponding job shop manufacturing system.     

Selection of a Threshold Value Based on Material Handling Cost in Machine-Component Grouping

Machine-component grouping is a basic step in the application of group technology to manufacturing. It is the process of finding families of similar parts (part-families) and forming the associated machine cells such that one or more part-families can be processed within a single cell. Among the algorithms used to form the machine cells, those based on the Similarity Coefficient Method (SCM) are more flexible in incorporating the manufacturing data into the machine-component grouping process. SCM is the application of clustering techniques in forming the machine cells. One of the major problems with SCM is that it generates a set of alternative solutions rather than a unique solution. The number and size of machine cells in a given solution depends upon the similarity coefficient (threshold value) at which machine cells/machines are joined. This paper discusses the problem of selecting a proper threshold value and presents a solution to it.     

Application of the Similarity Coefficient Method in Group Technology

The Similarity Coefficient Method (SCM) is one of the methods used to form the machine cells in group technology applications. Compared to the other methods, SCM incorporates more flexibility into the machine-component grouping process and more easily lends itself to the computer application. The new model improves the existing models based on SCM by dealing with the duplication of bottleneck machines and by employing special data storage and analysis techniques which greatly simplify the machine-component grouping process. The duplication process in the new model is based on the number of inter-cellular moves. Duplication starts with the machine generating the largest number of inter-cellular moves and continues until no machine generates more inter-cellular moves than specified by a threshold value. By changing the threshold value, alternative solutions can be examined. the new model employs the bit-level data storage technique to reduce the storage and computational requirements of the machine-component grouping process.     

Component grouping for GT applications—a fuzzy clustering approach with validity measure

The variety of the currently available component grouping methodologies and algorithms provide a good theoretical basis for implementing GT principles in cellular manufacturing environments. However, the practical application of the grouping approaches can be further enhanced through extensions to the widely used grouping algorithms and the development of criteria for partitioning components into an ‘optimum’ number of groups. Extensions to the fuzzy clustering algorithm and a definition of a new validity measure are proposed in this paper. These are aimed at improving the practical applicability of the fuzzy clustering approach for family formation in cellular manufacturing environments. Component partitioning is based upon assessing the compactness of components within a group and overlapping between the component groups. The developed grouping methodology is experimentally demonstrated using an industrial case study and several well known component grouping examples from the published literature.     

Lexicographic fuzzy multi-objective model for minimisation of exceptional and void elements in manufacturing cell formation

This research proposes a lexicographic fuzzy multi-objective model based on perfect grouping for concurrent solving the part-family and machine-cell formation problems in a cellular manufacturing system. New simplified mathematical expressions of exceptional and void elements are proposed, opposing conventional quadratic and absolute functions. The main objectives of the proposed solution model, that is, the minimisation of both the number of exceptional elements and the number of void elements is defined by fuzzy goals as pre-emptive ordering. A lexicographic fuzzy goal model is developed to enhance cell performance and machine utilisation simultaneously. A satisfactory efficient solution can easily be obtained, and alternative solutions can also be generated by capturing flexibility of the proposed fuzzy multi-objective programming model. The formulated model can be solved by existing integer programming solvers. Finally, the evaluation of cell formation problems is briefly discussed to show the performance of the proposed model.     

Cellular machine layout based on the Segmented Flow Topology

This paper introduces a facility layout design procedure for converting an existing manufacturing system with a predefined aisle structure to a cellular manufacturing system based on the 'segmented flow topology' (SFT) developed by Sinriech and Tanchoco. The proposed procedure is aimed at finding the best machine grouping, along with the locations of pick-up and delivery stations and machine layout for each cell based on an existing facility. The objective is to minimize the total material handling cost. In contrast to previous work in this area, the proposed design procedure takes into account both distance and material flow in forming machine clusters. In addition, a revised cost model for material handling system, which accounts for different aspects of capital and operating cost, is presented.     

A correct minimal siphons extraction algorithm from a maximal unmarked siphon of a Petri net

When using the machine grouping approach for designing cellular manufacturing cells, improper machine assignment commonly arises resulting in higher intercellular movement of parts. The objective of this paper is therefore to examine the cause of such a problem and to propose a workable optimal solution for it. A machine grouping algorithm that is based in the new machine unit concept is developed and the solution is proven to be optimal at each stage. A comparison of the proposed model with two existing algorithms is presented, followed by a discussion on the behaviour of machine cell formation.     

A genetic algorithm for manufacturing cell formation with multiple routes and multiple objectives

This paper presents a genetic algorithm (GA) approach to the machine-component grouping problem with multiple objectives: minimizing costs due to intercell and intracell part movements; minimizing the total within cell load variation; and minimizing exceptional elements. Manufacturing cells are formed based on production data, e.g. part routing sequence, production volume and workload. Also, we will discuss the implication of part alternative routings and the method we suggest to deal with it. Special genetic operators are developed and multiple experiments are performed. Finally, the results obtained with the proposed algorithm on the tested problems are compared with those of others.     

Impact of throughput-based objectives and machine grouping decisions on the short-term performance of flexible manufacturing systems

The most commonly-used objective in the literature for solving machine loading and grouping problems in flexible manufacturing systems is the maximization of steady-state throughput. In reality, orders arrive dynamically and the mix of products changes frequently, raising questions about the applicability of this objective in achieving shorter-term measures of the output rate. Moreover, the loading and grouping problems only determine which operations are to be performed on each machine, and whether any of the machines should be identically tooled so as to allow multiple routeings for some jobs. The actual short-term performance of the system also depends on scheduling and dispatching decisions. We study the impact of the various objectives ostensibly related to steady-state throughput and machine grouping decisions on the short-term makespan performance (in a static setting). Computational results suggest that minimizing the maximum percentage overload (relative to the optimal continuous workload allocation) across machine groups is an excellent objective. The results also indicate that reducing the number of machine groups and balancing workloads among the machines help to reduce makespan.     

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.     

Impact of the use-based maintenance policy on the performance of cellular manufacturing systems

In this paper, a multi-objective integer programming approach is developed to investigate the impact of the use-based preventive maintenance (UPM) policy on the performance of the cellular manufacturing system (CMS). Under the UPM policy a maintenance schedule is established which provides for the performance of preventive maintenance (PM) only after a predetermined number of operating hours of machine use. This research indicates how PM and failure repair (FR) actions affect the effective availability of the machines and accordingly the machine and inter/intra-cell material handling costs under the UPM policy. The objective is to minimise the machine cost, inter- and intra-cell material handling and PM/FR costs. The proposed model is solved by an interactive fuzzy programming (IFP) approach to determine the best compromise solution from the decision maker point of view. IFP assumes that each objective function has a fuzzy goal and focuses on minimising the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. Compromise solutions are prioritised by two efficiency criteria, i.e. grouping efficiency and system availability. The performance of the proposed model is verified by a comprehensive numerical example.     

An integrated layout for group technology with in-process inventory costs

This paper considers the use of group technology concepts in designing production systems. A classification system is used to identify machine-component groupings which determine facility layout and production scheduling. A design approach is developed which aims at minimizing the operating cost of the production system, composed of setup cost and inventory carrying cost.     

Performance of Fuzzy ART neural network for group technology cell formation

This study investigates the performance of Fuzzy ART neural network for grouping parts and machines, as part of the design of cellular manufacturing systems. Fuzzy ART is compared with ART1 neural network and a modification to ART1, along with direct clustering analysis (DCA) and rank order clustering (ROC2) algorithms. A series of replicated clustering experiments were performed, and the efficiency and consistency with which clusters were identified were examined, using large data sets of differing sizes and degrees of imperfection. The performance measures included the recovery ratio of bond energy and execution times, It is shown that Fuzzy ART neural network results in better and more consistent identification of block diagonal structures than ART1, a recent modification to ART1, as well as DCA and ROC2. The execution times were found to be more than those of ART1 and modified ART1, but they were still superior to traditional algorithms for large data sets.     

Modeling of machine failures in a flexible manufacturing cell with two machines served by a robot

In this study, a stochastic model is developed to analyze performance measures of a flexible manufacturing cell (FMC) under different operational conditions, including machine failures and repairs. The FMC consists of two machines served by a robot for loading and loading purposes, and a pallet handling system. The model is based on Markov processes and determines closed-form solutions for the probabilities of system states that are used to calculate system performance measures, such as production output rate and utilizations of system components under different parametric conditions and equipment failures and repairs.     

Feasibility and robustness of transiently chaotic neural networks applied to the cell formation problem

Cell formation is a key issue in the design of cellular manufacturing systems. Effective grouping of parts and machines can improve considerably the performance of manufacturing cells. The transiently chaotic neural network (TCNN) is a recent methodology in intelligent computation that has the advantages of both the chaotic neural network and the Hopfield neural network. The present paper investigates the dynamics of the TCNN network and studies the feasibility and robustness of final solutions of TCNN when applied to the cell formation problem. The paper provides insight into the feasibility and robustness of TCNN for cell formation problems. It also discusses how to set the initial values of the TCNN parameters in the case of well-structured and ill-structured cell formation problems.     

Machine-component group formation: an heuristic method for flexible production cells and flexible manufacturing systems

This paper provides an heuristic for the planning and study of machine-component groups in flexible production cells and flexible manufacturing systems. The problem of group formation defined on master-component process routes is undertaken in terms of minimum differences between masters and maximum combinations of masters. Group formation is a ‘hard’ combinatorial problem subject to exponential growth of complexity as the number of decision-making variables increases. The heuristic is designed to search the solution space of the problem in monotone-increasing order of solution costs so as to avoid the enumeration of solutions for cost minimization.