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 multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in defining the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the inter-cellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.     

A hybrid method based on genetic algorithm and dynamic programming for solving a bi-objective cell formation problem considering alternative process routings and machine duplication

Cell formation is one of the first and most important steps in designing a cellular manufacturing system. It consist of grouping parts with similar design features or processing requirements into part families and associated machines into machine cells. In this study, a bi-objective cell formation problem considering alternative process routings and machine duplication is presented. Manufacturing factors such as part demands, processing times and machine capacities are incorporated in the problem. The objectives of the problem include the minimization of the total dissimilarity between the parts and the minimization of the total investment needed for the acquisition of machines. A normalized weighted sum method is applied to unify the objective functions. Due to the computational complexity of the problem, a hybrid method combining genetic algorithm and dynamic programming is developed to solve it. In the proposed method, the dynamic programming is implemented to evaluate the fitness value of chromosomes in the genetic algorithm. Computational experiments are conducted to examine the performance of the hybrid method. The computations showed promising results in terms of both solution quality and computation time.     

Part-machine grouping using weighted similarity coefficients

The first step in the transition to cellular manufacturing is part-machine grouping. In this paper, grouping parts into families and machines into cells is done in two phases: by first grouping machines and then assigning parts. Limits both on the number of machines per cell and on the number of parts per family are considered. The number of cells is not fixed. A weighted sum of within-cell voids and out-of-cell operations is used to evaluate the part-machine grouping obtained. In Phase One, weighted similarity coefficients are computed and machines are clustered using a Tabu search algorithm. In Phase Two, part types are assigned to the previously formed groups using a linear minimum cost network flow model. The proposed approach is compared with three heuristics, namely ZODIAC, GRAFICS and MST, on a large number of problems.     

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.     

An improved genetic heuristic to support the design of flexible manufacturing systems

In industry, when flexible manufacturing systems are designed within a group technology approach, numerous decision-taking processes emerge requiring control of the multiple characteristics of the system. In this context, several grouping problems are identified within the scope of combinatorial optimisation. Such is the case of the part families with precedence constraints problem, which is defined in order to set up families where the total dissimilarity among the parts placed in the same family is minimal and precedence constraints, as well as capacity constraints arise when grouping parts. The present paper describes the use of an improved genetic heuristic to tackle this problem. It comprises a standard genetic heuristic with appropriate operators, improved through specific local search. In order to study the performance of the improved genetic approach, a special purpose constructive heuristic plus an earlier version of the genetic heuristic were implemented. CPLEX software was used from a binary linear formulation for this problem. Computational results are given from the experiment performed using test instances partly taken from the literature while others were semi-randomly generated. The improved genetic heuristic produced optimal solutions for most of the shortest dimension test instances and acted positively in relation to the constructive heuristic results, over almost all the instances. As for the CPLEX it found optimal solutions only for the small instances, besides which for the higher dimensioned instances CPLEX failed to obtain any integer solutions at all, in 10h running time. Therefore, these experiments demonstrate that the improved genetic is a good tool to tackle high dimensioned test instances, when one does not expect an exact method to find an optimal solution in reasonable computing time.     

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.     

Analysis of efficiency measures for block diagonal machine-component charts

Group efficiency measures have been developed to evaluate machine-component charts for the formation of cellular manufacturing systems. In this paper the existing grouping efficiency measures will be evaluated by determining the relationship between the values of a grouping efficiency measure and the performance of the corresponding cellular manufacturing system.     

A design methodology for configuration of manufacturing cells

This paper presents a general design methodology for manufacturing cells. The approach makes use of the observation that 85% of the production demand of a manufacturing facility can be attributed to 15% of the products manufactured in the facility. This logic was extended to manufacturing cell design. Specifically, within a part family, those parts that have a high steady demand should be placed in cells that are configured and operated similar to a flow line. Those part numbers within the family that have little demand should be assigned to cells designed to operate more as a job shop. In this way, a manufacturing cell that is designed to serve both the high and low demand components will not be impeded by imposed constraints resulting from demand or processing time considerations for individual parts within the family. The author presents a ten step approach for analysis and design of such cells after initial machine-component groupings have been formed.     

A hybrid genetic-variable neighborhood search algorithm for the cell formation problem based on grouping efficacy

Cell formation problem attempts to group machines and part families in dedicated manufacturing cells such that the number of voids and exceptional elements in cells are minimized. In this paper, we presented a linear fractional programming model with the objective of maximizing the grouping efficacy while the number of cells is unknown. To show the effectiveness of the proposed model, two test problems were applied. Then, to solve the model for real-sized applications, a hybrid meta-heuristic algorithm in which genetic algorithm and variable neighborhood search are combined. Using the grouping efficacy measure, we have also compared the performance of the proposed algorithm on a set of 35 test problems from the literature. The results show that the proposed GA-VNS method outperforms the state-of-the-art algorithms.     

Machine cell formation considering processing times and machine capacities: An ortho-synapse Hopfield neural network approach

The machine/part grouping problems can be classified into binary and comprehensive grouping problems depending on whether or not the processing times and the machine capacities are considered. The binary grouping problem arises if the part demands are unknown when the CMS is being developed. If the part demand can be forecast accurately, both the processing times and machine capacities have to be included in the analysis. This gives rise to comprehensive grouping. Both the binary and comprehensive grouping have been proven to be NP-complete which cannot be solved in polynomial time. Considering the large number of parts and machines involved in the industrial design problem, efficient solution methods are highly desirable. In this paper, a new neural network approach (OSHN_g) is proposed to solve the comprehensive grouping problems. The proposed approach has been tested on 28 test problems. The results show that the OSHN_g method is very efficient and its solution quality is comparable to that of a simulated annealing approach.     

Manufacturing cell formation with production data using neural networks

Batch type production strategies need adoption of cellular manufacturing (CM) in order to improve operational effectiveness by reducing manufacturing lead time and costs related to inventory and material handling. CM necessitates that parts are to be grouped into part families based on their similarities in manufacturing and design attributes. Then, machines are allocated into machine cells to produce the identified part families so that productivity and flexibility of the system can be improved. Zero-one part-machine incidence matrix (PMIM) generated from route sheet information is commonly presented as input for clustering of parts and machines. An entry of ‘1’ in PMIM indicates that the part is visiting the machine and zero otherwise. The output is generated in the form of block diagonal structure where each block represents a machine cell having more than one machines and a part family. The major limitations of this approach lies in the fact that important production factors like operation time, sequence of operations, and lot size of the parts are not accounted for. In this paper, an attempt has been made to propose a clustering methodology based on adaptive resonance theory (ART) for addressing these issues. Initially, a methodology considering only the operation sequence of the parts has been proposed. Then, the methodology is suitably modified to deal with combination of operation sequence and operation time of the parts to address generalized cell formation (CF) problem. A new performance measure is proposed to quantify the performance of the proposed methodology. The performance of the proposed algorithm is tested with benchmark problems from open literature and the results are compared with the existing methods. The results clearly indicate that the proposed methodology outperforms the existing methods in most cases.     

Design of parts for cellular manufacturing using neural network-based approach

A neural network approach is applied to the problem of integrating design and manufacturing engineering. The self organising map (SOM) neural network recognizes products and parts which are modeled as boundary representation (B-rep) solids using a modified face complexity code scheme adopted, and forms the necessary feature families. Based on the part features, machines, tools and fixtures are selected. These information are then fed into a four layer feed-forward neural network that provides a designer with the desired features that meet the current manufacturing constraints for design of a new product or part. The proposed methodology does not involve training of the neural networks used and is seen to be a significant potential for application in concurrent engineering where design and manufacturing are integrated.     

Simulation-based methodology for machine cell design

This paper presents a simulation-based methodology which uses both design and manufacturing attributes to form manufacturing cells. The methodology is implemented in three phases. In phase I, parts are grouped into part families based on their design and manufacturing dissimilarities. In phase II, machines are grouped into manufacturing cells based on relevant operational costs and various cells are assigned part families using an optimization technique. Phases I and II are based on integer and mixed-integer mathematical models. Finally, in phase III, a simulation model of the proposed system is built and verified, and the model is run so that data on the proposed system may be gathered and evaluated. The mathematical and simulation models are used to solve a sample production problem. The results from these models are compared, and can be used to justify the final design. By the use of these modeling tools, cellular manufacturing systems can be designed, analyzed, optimized, and finally justified.     

Grouping efficiency measures and their impact on factory measures for the machine-part cell formation problem: A simulation study

Over the past 25 years, the machine-part cell formation problem has been the subject of numerous studies. Researchers have applied various methodologies to the problem in an effort to determine optimal clusterings of machines and optimal groupings of parts into families. The quality of these machine and part groupings have been evaluated using various objective functions, including grouping efficacy, grouping index, grouping capability index, and doubly weighted grouping efficiency, among others. In this study, we investigate how appropriate these grouping quality measures are in determining cell formations that optimize factory performance. Through the application of a grouping genetic algorithm, we determine machine/part cell formations for several problems from the literature. These cell formations are then simulated to determine their impact on various factory measures, such as flow time, wait time, throughput, and machine utilization, among others. Results indicate that it is not always the case that a “more efficient” machine/part cell formation leads to significant changes or improvements in factory measures over a “less efficient” cell formation. In other words, although researchers are working to optimize cell formations using efficiency measures, cells formed this way do not always demonstrate optimized factory measures.     

A mathematical approach for the formation of manufacturing cells

One of the major steps in designing cellular manufacturing systems is to form cells. This involves identification of machine cells and part families. This paper proposes a new mathematical approach for forming manufacturing cells. The proposed approach involves two phases. In the first phase, machine cells are identified by applying factor analysis to the matrix of similarity coefficients. In the second phase, an integer-programming model is used to assign parts to the identified machine cells. To evaluate its performance, the proposed approach was applied to sample problems from the literature and a real life problem from a manufacturing plant. The results indicate that the proposed approach performs very well in terms of a number of criteria and compares favorably to well-know existing cell formation methods. In addition to its good performance, the proposed approach has the flexibility to allow the cell designer to either identify the required number of cells in advance, or consider it as a dependent variable. Using algorithms which are available in many commercial software packages is the other advantage of the proposed approach.     

A methodology of forming manufacturing cells using manufacturing and design attributes

This study develops a methodology for forming machine cells using part's design and manufacturing dissimilarities. The proposed methodology is divided into two sequential phases. In phase I parts are grouped into families based upon their design and manufacturing attributes. In phase II, the machines are grouped into manufacturing cells based on relevant operational costs and the various cells are assigned part families using an optimization technique.     

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 cellular manufacturing system based on new similarity coefficient which considers alternative routes during machine failure

A two-phase procedure for configuring a cellular manufacturing system is proposed. In Phase I, a new similarity coefficient which considers the number of alternative routes when available during machine failure is proposed. The objective of Phase I is to identify part families based on the proposed new similarity coefficient. In Phase II, a new methodology which simultaneously considers scheduling and operational aspects in the cell design during machine failure for a manufacturing environment is proposed. Phase II shows how the scheduling and operational aspects influence the resource utilization during machine failure. The objective of the proposed methodology is to minimize the total sum of inventory holding cost, early/late finish penalty cost for each part in a given period, operating cost and machine investment cost by grouping machines into cells.