Part family formation for GT applications based on fuzzy mathematics

Group Technology (GT) is one of the key issues in a successful implementation of flexible manufacturing systems ( FMSs). The objective of GT is, through the use of a part-family (PF) formation scheme, to reduce unnecessary variation proliferation. A part family is a group of parts presenting similar geometry and/or requiring a similar production process. Traditional schemes such as classification and coding and production flow analysis do not consider uncertainty or impreciseness in PF formation. In order to incorporate the uncertainty which is inherent in the measurement of similarities between parts, fuzzy mathematics is employed in this research. Two different approaches of fuzzy cluster analysis, fuzzy classification and fuzzy equivalence, are introduced in the process of part-family formation. In addition, a dynamic part-family assignment procedure is presented using the methodology of fuzzy pattern recognition to assign new parts to existing PFs. A computer program is developed, and several rotational parts from a local company have been tested with satisfactory results. In this paper the theoretical foundation is detailed, along with real world examples.     

A rapid knowledge-based partial supervision fuzzy c-means for brain tissue segmentation with CUDA-enabled GPU machine

The proposed work aims to quicken the magnetic resonance imaging (MRI) brain tissue segmentation process using knowledge-based partial supervision fuzzy c-means (KPSFCM) with graphics processing unit (GPU). The proposed KPSFCM contains three steps: knowledge-based initialization, modification, and optimization. The knowledge-based initialization step extracts initial centers from input MR images for KPSFCM using Gaussian-based histogram smoothing. The modification step changes the membership function of PSFCM, which is guided by the labeled patterns of cerebrospinal fluid portion. Finally, the optimization step is achieved through size-based optimization (SBO), adjacency-based optimization (ABO), and parallelism-based optimization (PBO). SBO and ABO are algorithmic level optimization techniques in central processing unit (CPU), whereas PBO is a hardware level optimization technique implemented in GPU using compute unified device architecture (CUDA). Performance of the KPSFCM is tested with online and clinical datasets. The proposed KPSFCM gives better segmentation accuracy than 14 state-of-the-art-methods, but computationally expensive. When the optimization techniques (SBO and ABO) were included, the execution time reduces by 13 times in CPU. Finally, the inclusion of PBO yields 19 times faster than the optimized CPU implementation.     

GT cell formation for minimizing the intercell parts flow

A methodology is proposed to design a GT cell by considering the intercell parts flow in GT cellular manufacturing systems. The problem of GT cell formation is described in a graph using the quantities to be produced in the specified time period and the process routes for producing the products. The objective of this paper is to minimize the total number of parts produced in more than one cell. The problem, formulated as a quadratic assignment problem (QAP), is solved using both Lagrangean relaxation technique and the optimality conditions of quadratic program. Furthermore, in order to obtain the giobal optimal solution rather than the local optimal solution, a branch-and-bound algorithm is employed. Finally, numerical examples are used to show the effectiveness of the solution techniques and GT cell formation procedure. Moreover, a computer simulation is presented, showing the effectiveness of cellular manufacturing systems     

A two-phase heuristic algorithm for cell formation problems considering alternative part routes and machine sequences

In this paper, we deal with the multi-objective machine cell formation problem. This problem is characterized as determining part route families and machine cells such that the total sum of inter-cell part movements and maximum machine workload imbalance are simultaneously minimized. Together with the objective function, alternative part routes and the machine sequences of part routes are considered in grouping part route families. Also, it is assumed that the number of machine cells is not pre-defined. Owing to the complexity of the problem, a two-phase heuristic algorithm is proposed. Computational experiments were conducted to verify the performance of the algorithm. Throughout the computational experiments, we verified that the two-phase heuristic algorithm is effective for large-scale machine cell formation problems.     

An experiment investigating the application of clustering procedures and similarity coefficients to the GT machine cell formation problem

This paper reports the development of a number of similarity-based coefficients designed for applying hierarchical cluster analysis to the group technology machine cell formation problem. The paper also discusses an experimental investigation applying these and other well-known similarity coefficients in conjunction with some well-known clustering algorithms. The mixture model experimental approach is used for the investigation. A number of problems were generated via simulation, randomly ‘mixed’ to hide the original cellular structure, and the clustering techniques applied. Extensions of prior research include the development of new similarity coefficients, their comparative evaluation, and the incorporation of the concept of part ‘weighting’ into the cluster analysis, and hence, cell formation     

Agent-based dynamic part family formation for cellular manufacturing applications

One of the main critique on cellular manufacturing and its algorithms is their inability to handle dynamics events, especially dynamic changes in part spectrum. Unfortunately, there are not many efforts in the literature to overcome this problem. Agent oriented computing provides a marvellous opportunity to handle dynamic problems and to provide effective solutions, if carefully and intelligently implemented. In this paper, we have proposed a novel agent-based clustering algorithm for part family formation in cellular manufacturing by considering dynamic demand changes. However, it is not easy to directly compare the performance of the proposed algorithm with the literature results as there is no benchmark for dynamic cell formation problems. We attempt to compare the performance of the present algorithm on static test problems by dynamically introducing parts in these data-sets to our algorithm. Many results have been presented on these static data-sets by utilising several heuristics, meta-heuristics and optimisation-based algorithms. Although the proposed algorithm is not an optimisation-based algorithm and its operation is directed to handle dynamic changes in the problem domain through negotiation, we have shown that it has ability to provide very good results which are comparable to the best known solutions.     

GT efficacy: a performance measure for cell formation with sequence data

Cell formation in cell manufacturing design is a crucial step for improving productivity. As input data for the cell formation problem, a part-machine incidence matrix is given in binary format or in ordinal format. The solutions for effective cell formation have to be compared using performance measures. Grouping efficacy is used as a standard measure for evaluating solutions based on a binary part-machine matrix, which does not consider ordinal data. However, the representative measures, called standard measures, for the ordinal part-machine matrix are absent. The existing measures designed for ordinal data produce conflicting results and can lead to subjective decisions. In this paper, a new objective measure called group technology (GT) efficacy is proposed for ordinal data, reflecting on both intercellular movement and compactness within cells simultaneously. The advantage of GT efficacy is demonstrated by comparing it with a few previously-proposed measures.     

Design of delayed reconfigurable manufacturing system based on part family grouping and machine selection

To improve the convertibility of reconfigurable manufacturing system (RMS), the concept of delayed reconfigurable manufacturing system (D-RMS) was proposed. RMS and D-RMS are both constructed around part family. However, D-RMS may suffer from ultra-long system problem with unacceptable idle machines using generic RMS part families. Besides, considering the complex basic system structure of D-RMS, machine selection of D-RMS should be addressed, including dedicated machine, flexible machine, and reconfigurable machine. Therefore, a system design method for D-RMS based on part family grouping and machine selection is proposed. Firstly, a part family grouping method is proposed for D-RMS that groups the parts with more former common operations into the same part family. The concept of longest relative position common operation subsequence (LPCS) is proposed. The similarity coefficient among the parts is calculated based on LPCS. The reciprocal value of the operation position of LPCS is adopted as the characteristic value. The average linkage clustering (ALC) algorithm is used to cluster the parts. Secondly, a machine selection method is proposed to complete the system design of D-RMS, including machine selection rules and the dividing point decision model. Finally, a case study is given to implement and verify the proposed system design method for D-RMS. The results show that the proposed system design method is effective, which can group parts with more former common operations into the same part family and select appropriate machine types.     

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.     

Efficient algorithm for cell formation with sequence data,machine replications and alternative process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Despite a large number of papers on cell formation being published, only a handful incorporate operation sequence in intercell move calculations and consider alternative process routings, cell size, production volume and allocating units of identical machines into different cells. Modelling the above factors makes the cell formation problem complex but more realistic. The paper develops a model and solution methodology for a problem of cell formation to minimize the sum of costs of intercell moves, machine investment and machine operating costs considering all the factors mentioned above. An algorithm comprised of simulated annealing and local search heuristics has been developed to solve the model. A limited comparison of the proposed algorithm with an optimal solution generated by complete enumeration of small problems indicates that the algorithm produces a solution of excellent quality. Large problems with 100 parts and 50 machine types are efficiently solved using the algorithm.     

Modeling and analysis of a manufacturing cell formation problem with fuzzy mixed-integer programming

Cell formation (CF) has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practice. In this paper a sophisticated mixed-integer programming (MIP) model is proposed to simultaneously form manufacturing cells and minimize the cost of dealing with exceptional elements. Also, we illustrate how a fuzzy mixed-integer programming (FMIP) approach can be used to solve the CF problem in a fuzzy environment, propose a new fuzzy operator, and examine the impact of different membership functions and operators on computational performance. Our study shows that FMIP not only provides a better and more flexible way of representing the problem domain, it also leads to improved overall performance.     

A clustering algorithm for machine cell formation in group technology using minimum spanning trees

I address the machine cell part family formation problem in group technology. The minimum spanning tree (MST) For machines is constructed from which seeds to cluster components are generated. Seeds to cluster machines are obtained from component clusters. The process of alternate seed generation and clustering is continued until feasible solutions are obtained. Edges are removed from the MST to identify alternate starting seeds for clustering. The algorithm is tested with matrices available in the literature. The results compare favourably with existing methods.     

A genetic algorithm for FMS part type selection and machine loading

Part type selection (PTS) and machine loading are two major problems in the production planning of flexible manufacturing systems. In this paper, we solve these problems by the use of genetic algorithms (GAs). We exploit the problem's MIP (mixed integer programming) model to make our GA more meaningful and less computation-intensive. The GA strategy is developed in three parts: solution coding, solution generation and solution recombination. In solution coding, we replace the original binary routing variables with integer variables and thus reduce the chromosome length significantly. In solution generation, the level of feasibility is the main concern. We divide the constraints into two categories: direct and indirect. The direct constraints involve only two variables each and are easily satisfied by context-dependent genes. Since the direct constraints form the major chunk of constraints, their satisfaction controls infeasibility to a large extent. The remaining indirect constraints are handled by the penalty function approach. The solution recombination involves crossover and mutation. The crossover is performed in two steps, the PTS swap followed by the routing swap, so that the feasibility level is not disturbed. With a similar intent, the mutation is allowed to operate only on selective genes. All the steps are illustrated with examples. Our GA is able to achieve optimum or near-optimum performance on a variety of objectives. A parametric study of GA factors is also carried out, indicating population size and mutation probability as influential parameters.     

Configuration design of scalable reconfigurable manufacturing systems for part family

Intense global competition, dynamic product variations, and rapid technological developments force manufacturing systems to adapt and respond quickly to various changes in the market. Such responsiveness could be achieved through new paradigms such as Reconfigurable manufacturing systems (RMS). In this paper, the problem of configuration design for a scalable reconfigurable RMS that produces different products of a part family is addressed. In order to handle demand fluctuations of products throughout their lifecycles with minimum cost, RMS configurations must change as well. Two different approaches are developed for addressing the system configuration design in different periods. Both approaches make use of modular reconfigurable machine tools (RMTs), and adjust the production capacity of the system, with minimum cost, by adding/removing modules to/from specific RMTs. In the first approach, each production period is designed separately, while in the second approach, future information of products’ demands in all production periods is available in the beginning of system configuration design. Two new mixed integer linear programming (MILP) and integer linear programming (ILP) formulations are presented in the first and the second approaches respectively. The results of these approaches are compared with respect to many different aspects, such as total system design costs, unused capacity, and total number of reconfigurations. Analyses of the results show the superiority of both approaches in terms of exploitation and reconfiguration cost.     

海上风机桩式基础结构形式综合模糊优选

针对海上风机基础设计中经常遇到复杂的方案优化选型问题,将多因素、多级模糊优选理论引入到基础的设计选型中。针对影响因素复杂、确定隶属函数主观因素较强的情况,成功引入因素的优先关系法来确定优选矩阵的隶属度,较好地解决了确定隶属函数的人为影响。通过此优选模型成功地将影响基础设计选型的13种主要因素和4种桩基基础设计形式进行了多级模糊综合优选决策,得到了比较理想的决策结果,为海上风机基础设计选型提供了新的思路。     

Non-linear programming for the optimization of machine repair problems in fuzzy environments

Machine repair models have wide applications in many systems such as production line systems and maintenance operations. A procedure is developed to derive the fuzzy objective value of the cost-based machine repair optimization problem, in that the cost coefficients and the machine breakdown rate are fuzzy numbers. On the basis of the extension principle, a pair of non-linear programs are formulated to calculate the lower and upper bounds of the fuzzy minimum expected total cost at the possibility level α. The membership function of the minimum expected total cost is constructed by enumerating different values of α. A numerical example is solved successfully to demonstrate the validity of the proposed method. Since the minimum expected total cost is completely expressed by a membership function rather than by a crisp value, the fuzziness of the input data is conserved, and more information is provided for decision-making. Furthermore, since the optimum repair rate obtained is fuzzy, a crisp optimum repair rate based on the Yager ranking indices is recommended for practical use.     

Integrated optimization of machine product design and process design

In order to attain the true integration of computer-aided design and computer-aided manufacturing not only is a smooth flow of information required, but also decision making for both product design and process design must be synthesized. In this paper an integrated design process is proposed in which decisions concerning both product design and process design are simultaneously made. According to the proposed design procedures, an integrated optimization problem is formulated. This optimization is expressed as a multiobjective optimization problem which produces many Pareto optimum solution sets corresponding to combinations of materials used for parts. The algorithm for solving the problem is also presented. The proposed method is applied to designing a cylindrical co-ordinate robot, thereby demonstrating the effectiveness of conducting a simultaneous process through product design and process design.     

The formation of subfamilies for machine loading of flow-line cells

Cellular manufacturing constitutes a key element of contemporary approaches to manufacturing such as just-in-time and flexible manufacturing systems. In some settings, it may not be possible to release all members of a family to a cell at the same time due to tooling, level of the in-process inventory and limited machine buffer capacity constraints. When such constraints exist, one approach is to partition each family into subfamilies.

In this research, we consider the effective partitioning of a family of parts into subfamilies in cyclically scheduled cells involving unidirectional material flow between machines laid out in a loop and where capacity constraints on tooling are present. The criterion for forming subfamilies is the minimization of the total machine idle time. This criterion maximizes the rate of output of the cell as well as maximizing machine utilization. A batching approach to part and tool change is utilized and all tool magazine set-ups are assumed lo occur off-line while the cell is in operation.

We present a model for the formation of subfamilies using the above criterion and develop four heuristic approaches based on it. We then examine their effectiveness by conducting computational experiments over a wide range of realistic situations generated by varying the characteristics of the operating environment, tooling requirements and tool magazine capacity.     

Decision support for design of reconfigurable rotary machining systems for family part production

To remain competitive in currently unpredictable markets, the enterprises must adapt their manufacturing systems to frequent market changes and high product variety. Reconfigurable manufacturing systems (RMSs) promise to offer a rapid and cost-effective response to production fluctuations under the condition that their configuration is attentively studied and optimised. This paper presents a decision support tool for designing reconfigurable machining systems to be used for family part production. The objective is to elaborate a cost-effective solution for production of several part families. This design issue is modelled as a combinatorial optimisation problem. An illustrative example and computational experiments are discussed to reveal the application of the proposed methodology. Insight gained would be useful to the decision-makers managing the configuration of manufacturing systems for diversified products.     

Integrated approach to part scheduling and inspection policies for a job shop manufacturing system

The quality of a product greatly depends on the quality of its components. This requires that manufacturing specifications have to be met in the manufacturing environment and as a consequence inspection stations are present in many manufacturing systems and inspection policies must be adopted. One problem, which has been widely investigated, concerns the detection of the inspection points in the hypothesis that the action to be taken is known when a defective part is detected. If different jobs are to be produced, then operation scheduling becomes yet another complex problem needing to be solved. And while the problem of scheduling has received a great amount of attention from researchers, to our knowledge the interaction between the two problems has not been treated in job-shop environment. In the present paper three different control policies are preliminarily examined: they differ both in terms of the number of operations that are inspected, and with regard to the type of intervention carried out on detection of a defect. Each control policy affects the optimal inspection locations, which, in their turn, influence operation scheduling. As will be shown in the present paper, a sequential decision process based on separate optimization steps can lead to very poor final results. For this reason, an integrated approach is proposed, in an attempt to identify an optimal solution using a genetic algorithm.