A machine level based-similarity coefficient for forming manufacturing cells

In cellular manufacturing literature, early work heavily focused on the use of routers as a way of forming product families and manufacturing cells. later, several measures of similarity among machines and parts have been proposed by different authors. Similarity coefficients use more data in addition to the machines visited. In this paper, a new similarity coefficient is proposed which incorporates not only the type of machines but also the level (number) of machines required. The potential benefits of the proposed similarity coefficient is demonstrated with an example problem using three clustering algorithms namely, SLINK, ALINK and CLINK. The results obtained by using this new similarity coefficient are compared with those of the Jaccard's Similarity Coefficient.     

A dynamic programming based heuristic for machine grouping in manufacturing cell formation

This paper presents a two stage heuristic for solving the machine grouping problem encountered in the identification and formation of flexible manufacturing cells. A new similarity measure, called Cell Bond Strength, is introduced which exploits similarity in processing for pairs of machines. The Cell Bond Strengths values are calculated based upon part routing and production requirements data. In the first stage of the heuristic, a Dynamic Programming procedure is used to determine an optimum “chain” of machines in which the sum of the bonds between machines is maximized. The second stage of this heuristic partitions the chain to form machine cells, subject to cell size restrictions. An example is given to illustrate the heuristic procedure. A summary of computational results when the heuristic is applied to several examples from the literature is also presented.     

The cell formation problem in cellular manufacturing systems—A sequential modelling approach

A sequential modelling approach to the cell formation problem in cellular manufacturing systems is presented in this paper. First, the machines are grouped into cells based on their similarity in parts processing; next the parts are allocated to appropriate machine groups based on the processing requirements. The machine grouping and the parts allocation problems are modelled as 0–1 integer programs. The application of the models is illustrated using a numerical example.     

A comparative study of similarity measures for manufacturing cell formation

We introduced a spectral clustering algorithm based on the bipartite graph model for the Manufacturing Cell Formation problem in [Oliveira S, Ribeiro JFF, Seok SC. A spectral clustering algorithm for manufacturing cell formation. Computers and Industrial Engineering. 2007 [submitted for publication]]. It constructs two similarity matrices; one for parts and one for machines. The algorithm executes a spectral clustering algorithm on each separately to find families of parts and cells of machines. The similarity measure in the approach utilized limited information between parts and between machines. This paper reviews several well-known similarity measures which have been used for Group Technology. Computational clustering results are compared by various performance measures.     

Clustering algorithm for solving group technology problem with multiple process routings

Cell formation is an important problem in the design of a cellular manufacturing system. Most of the cell formation methods in the literature assume that each part has a single process plan. However, there may be many alternative process plans for making a specific part, specially when the part is complex. Considering part multiple process routings in the formation of machine-part families in addition to other production data is more realistic and can produce more independent manufacturing cells with less intercellular moves between them. A new comprehensive similarity coefficient that incorporates multiple process routings in addition to operations sequence, production volumes, duplicate machines, and machines capacity is developed. Also, a clustering algorithm for machine cell formation is proposed. The algorithm uses the developed similarity coefficient to calculate the similarity between machine groups. The developed similarity coefficient showed more sensitivity to the intercellular moves and produced better machine grouping.     

Cell formation using multiple process plans

Cell formation (CF) consists of identifying machine groups and part families. Many CF procedures use a part machine matrix as an input and attempt to obtain a block diagonal form. But perfect block diagonalization of parts and machines is not possible is many cases. In this paper we consider a generalized cellular manufacturing (CM) problem, in which each part can have alternate process plans and each operation can be performed on alternate machines. Under these conditions the CF problem of assigning parts and machines to each manufacturing cell can be considered as a two stage process. The first stage deals with the problem of determining a unique process plan for each part. The second stage determines the part families and machine cells. In this research a model for forming part families and machine cells is presented considering alternate process plans. The objective is to analyze how alternate process plans influence and enhance the CM process giving better flexibility to the designer while designing cells for CM.     

An e-Learning tool considering similarity measures for manufacturing cell formation

Manufacturing cell formation is the first step in the design of cellular manufacturing system. The primary objective of this step is to cluster machines into machine cells and parts into part families so that the minimum of intercell trips will be achieved. This paper will be focused on the configuration of machine cells considering three types of initial machine-part matrix: binary (zero-one) matrix, production volume matrix, and operation time matrix. The similarity measure uses only information from these types of matrix. A pure combinatorial programming formulation will be developed to maximize the sum of similarity coefficients between machine/part pairs. An e-Learning tool/application to help industrial students and engineers for enhancing their cell formation capability is proposed. This tool is designed to include a novel similarity coefficient-based heuristic algorithm for solving the cell formation problem. To determine the performance of the proposed tool, comparison is made with a well-known tool along a case study.     

A novel approach to determine cell formation,intracellular machine layout and cell layout in the CMS problem based on TOPSIS method

This paper deals with the cellular manufacturing system (CMS) that is based on group technology (GT) concepts. CMS is defined as identifying the similar parts that are processed on the same machines and then grouping them as a cell. The most proposed models for solving CMS problems are focused on cell formation and intracellular machine layout problem while cell layout is considered in few papers. In this paper we apply the multiple attribute decision making (MADM) concept and propose a two-stage method that leads to determine cell formation, intracellular machine layout and cell layout as three basic steps in the design of CMS. In this method, an initial solution is obtained from technique for order preference by similarity to the ideal solution (TOPSIS) and then this solution is improved. The results of the proposed method are compared with well-known approaches that are introduced in literature. These comparisons show that the proposed method offers good solutions for the CMS problem. The computational results are also reported.     

Manufacturing cell formation using a new self-organizing neural network

Cellular manufacturing consists of grouping similar machines in cells and dedicating each of them to process a family of similar part types. In this paper, grouping parts into families and machines into cells is done in two steps: first, part families are formed and then machines are assigned. In phase one, weighted similarity coefficients are computed and parts are clustered using a new self-organizing neural network. In phase two, a linear network flow model is used to assign machines to families. To test the proposed approach, different problems from the literature have been solved. As benchmarks we have used a Maximum Spanning Tree heuristic.     

A simulated annealing method for solving a new mathematical model of a multi-criteria cell formation problem with capital constraints

One of the most important stages in the establishment of a cellular manufacturing system is the formation of manufacturing cells in order to find out which machines dedicated to each cell and part families corresponding to these machines. In this paper, two kinds of cells are being considered: (1) general or common cells which are able to manufacture different kinds of products and (2) specific cells which are able to manufacture a specific type of product. To set up cells for manufacturing, two kinds of capital constraints are observed: (1) capital constraints for construction and formation of cells and (2) capital availability constraints for the provision of tools and equipment to manufacture corresponding commodities. To find cells and to specify the family of the assigned commodities to each cell, different and various criteria exist. In this paper, three criteria are taken into consideration simultaneously in order to minimize the sum of: (1) costs of the delay in delivering a product to costumers by the above two cells in each period, (2) costs of the common and specific cells to remain idle in each period and (3) the unused capital. Since the cell formation problem is mostly time consuming, i.e. these are NP-hard, then to solve the problem, an effective algorithm of simulated annealing (SA) method is utilized. To verify and validate the efficiency of the SA algorithm, from the standpoint of the quality of the solution obtained and time of calculations, the results obtained are compared with those of the Lingo 6 software. Results suggest that the SA algorithm have good ability of solving the problem, especially in the case of large-sized problems for which Lingo 6 cannot produce solutions.     

Fuzzy-set-based machine-cell formation in cellular manufacturing

In cellular manufacturing, manufacturing cells are designed based on the assumption that only one machine is used for a particular operation. However, there can be alternative machines to process an operation. In this article, a fuzzy-set-based machine-cell formation algorithm for cellular manufacturing is presented. The fuzzy logic is employed to express the degree of appropriateness when alternative machines are specified to process a part shape. For machine grouping, the similarity-coefficient-based approach is used. The algorithm produces efficient machine cells and part families, which maximize the similarity values. This algorithm can also be used when the intercellular movement costs should be minimized. A numerical example is given to illustrate this approach.     

A bacteria foraging algorithm based cell formation considering operation time

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families based on pertinent similarity measures. The bacteria foraging algorithm (BFA) is a new in development computation technique extracted from the social foraging behavior of Escherichia coli (E. coli) bacteria. Ever since Kevin M. Passino invented the BFA, one of the main challenges has been employment of the algorithm to problem areas other than those for which the algorithm was proposed. This research work inquires the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, a newly developed BFA-based optimization algorithm for CF is discussed. In this paper, an attempt is made to solve the cell formation problem meanwhile taking into consideration number of voids in cells and a number of exceptional elements based on operational time of the parts required for processing in the machines. The BFA is suggested to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as similarity coefficients methods (SCM), rank order clustering (ROC), ZODIAC, GRAFICS, MST, GATSP, GP, K-harmonic clustering (KHM), K-means clustering, C-link clustering, modified ART1, GA (genetic algorithm), evolutionary algorithm (EA), and simulated annealing (SA) using defined performance measures known as modified grouping efficiency and grouping efficacy. The results lie in favor of better performance of the proposed algorithm.     

Development of bacteria foraging optimization algorithm for cell formation in cellular manufacturing system considering cell load variations

The cellular manufacturing system (CMS) is considered as an efficient production strategy for batch type production. The CMS relies on the principle of grouping machines into machine cells and grouping machine parts into part families on the basis of pertinent similarity measures. The bacteria foraging optimization (BFO) algorithm is a modern evolutionary computation technique derived from the social foraging behavior of Escherichia coli bacteria. Ever since Kevin M. Passino invented the BFO, one of the main challenges has been the employment of the algorithm to problem areas other than those of which the algorithm was proposed. This paper investigates the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem. In addition, for this purpose matrix-based bacteria foraging optimization algorithm traced constraints handling (MBATCH) is developed. In this paper, an attempt is made to solve the cell formation problem while considering cell load variations and a number of exceptional elements. The BFO algorithm is used to create machine cells and part families. The performance of the proposed algorithm is compared with a number of algorithms that are most commonly used and reported in the corresponding scientific literature such as K-means clustering, the C-link clustering and genetic algorithm using a well-known performance measure that combined cell load variations and a number of exceptional elements. The results lie in favor of better performance of the proposed algorithm.     

BASE: A bacteria foraging algorithm for cell formation with sequence data

A cellular manufacturing system (CMS) is considered an efficient production strategy for batch type production. A CMS relies on the principle of grouping machines into machine cells and grouping parts into part families on the basis of pertinent similarity measures. The bacteria foraging algorithm (BFA) is a newly developed computation technique extracted from the social foraging behavior of Escherichia coli (E. coli) bacteria. Ever since Kevin M. Passino invented the BFA, one of the main challenges has been employment of the algorithm to problem areas other than those for which the algorithm was proposed. This research work studies the first applications of this emerging novel optimization algorithm to the cell formation (CF) problem considering the operation sequence. In addition, a newly developed BFA-based optimization algorithm for CF based on operation sequences is discussed. In this paper, an attempt is made to solve the CF problem, while taking into consideration the number of voids in the cells and the number of inter-cell travels based on operational sequences of the parts visited by the machines. The BFA is suggested to create machine cells and part families. The performance of the proposed algorithm is compared with that of a number of algorithms that are most commonly used and reported in the corresponding scientific literature, such as the CASE clustering algorithm for sequence data, the ACCORD bicriterion clustering algorithm and modified ART1, and using a defined performance measure known as group technology efficiency and bond efficiency. The results show better performance of the proposed algorithm.     

Manufacturing cell formation in the presence of flexible cell locations and material transporters

The effect of cell locations and material transporters in the formation of manufacturing cells is investigated in this paper. Automated guided vehicles (AGVs) using a tandem configuration are considered and a first-come-first-served (FCFS) principle is applied for transporting the material between machines or between the input/output (I/O) and a machine. Using the time taken to perform material transfers as a suitable measure, a polynomial programming model is developed for the problem. As the model can be shown strongly NP-hard, a higher-level heuristic algorithm based upon a concept known as ‘tabu search’ is presented. An example problem is solved to further demonstrate that cell locations indeed have a significant impact when material transfers are used in the design of manufacturing cells.     

Machine-component grouping using genetic algorithms

One major problem in cellular manufacturing is the grouping of component parts with similar processing requirements into part families, and machines into manufacturing cells to facilitate the manufacturing of specific part families assigned to them. The objective is to minimize the total inter-cell and intra-cell movements of parts during the manufacturing process. In this paper, a mathematical model is presented to describe the characteristics of such a problem. An approach based on the concept of genetic algorithms is developed to determine the optimal machine-component groupings. Illustrative examples are used to demonstrate the efficiency of the proposed approach. Indeed, the results obtained show that the proposed genetic approach is a simple and efficient means for solving the machine-component grouping problem.     

差分进化算法求解带批处理机的机器人制造单元调度问题

机器人制造单元是智能制造系统的主要载体,研究机器人制造单元的生产调度问题对于提高智能制造系统的生产效率有着重要作用.对此,研究带批处理机的混合流水线机器人制造单元调度问题.首先,针对机器人制造单元与批处理机的生产特性,建立数学优化模型;其次,设计差分进化算法对其进行求解,提出染色体组编码的概念,求解该问题的染色体组由两个染色体构成,第1条染色体确定工件在每个工序选择的机器,第2条染色体确定加工顺序以及机器人的搬运顺序;然后,设计差分变异、交叉以及选择操作;最后,进行数值实验,结果证明,针对带批处理机的机器人制造单元调度问题,差分进化算法能缩短完工时间,得到更好的解.     

Models for cell loading and product sequencing in labor-intensive cells

This paper focuses on cell loading issues and product sequencing in labor-intensive cells. In labor-intensive cells, there are usually more operators than number of operations and cells usually consists of simple and light-weight machines and equipment. A three-phase methodology is proposed to deal with this problem. The objectives considered are minimizing makespan, total machine requirements, and intra-cell manpower transfers. In the first phase, optimal manpower allocation to operations is determined for each product. Then, similarity among products is established based on the similarity of operator/machine levels. The second phase involves cell loading to minimize makespan and machine requirements. Two mathematical models are developed to accomplish these tasks. One mathematical model (model A) does not allow product splitting whereas model B allows product splitting. Finally, third phase treats the product sequencing problems as traveling salesman problem (TSP) where the objective is to minimize intra-cell manpower transfers. Experimentation is performed and the results are compared with those of a heuristic procedure developed earlier. The results show that model A or model B can be chosen over the heuristic procedure.     

CLASS: An algorithm for cellular manufacturing system and layout design using sequence data

Cell formation problem in CMS design has received the attention of researchers for more than three decades. However, use of sequence data for cell formation has been a least researched area. Sequence data provides valuable information about the flow patterns of various jobs in a manufacturing system. Therefore, it is only natural to expect that use of sequence data must result in not only identifying the part families and machine groups but also the layout (sequence) of the machines within each cell. Unfortunately, such an approach has not been taken in the past while solving CMS design problem using sequence data. In this paper, we fill this gap in the literature by developing an algorithm that not only identifies the cells but also the sequence of machines in the cells in a simultaneous fashion. The numerical computations of the algorithm with the available problems in the literature indicate the usefulness of the algorithm. Further, it also points to the untapped potential of such an approach to solve CMS design and layout problem using sequence data.     

Machine assignment and part-families formation using group technology

The conventional way of solving the group technology (GT) problem is to start from an assignment of parts to machines and try to find a partitioning of machine cells and part families. The similarity between parts is measured based on commonality of the machines assigned to them. However, parts are assigned to machines based on their operation requirements and the operation capabilities of machines. Similarity between parts should be based on their required operations. In this paper, the authors attempt to solve or facilitate solving the GT problem at the assignment level. An algorithm for assigning parts to machines is provided which utilizes the types of operations required by parts and applies GT principles in producing the assignment. This leads to better partitioning of machine cells and part-families. Furthermore, operation sequences required by parts in determining the similarity between parts have been considered. An algorithm to form part-families based on the operation sequence similarity coefficient has been developed. The resulting families are then used by the assignment algorithm to produce machine assignments to part-families. The use of the algorithm is demonstrated by examples.