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.     

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.     

Product mix strategy and manufacturing flexibility

The manufacturing industry is facing a turbulent and constantly changing environment, with growing complexity and high levels of customisation. Any investment solution should address these problems for a dynamic market and within limited budget boundaries, so that companies try to remain competitive. The authors propose a real options model to support firms making important investment decisions, specifically decisions associated with the acquisition of new equipment aimed at allowing firms to increase their manufacturing flexibility for the production of both standard and customized products. This paper is partially based on a real operating experience related to visual finishing technology features in an industrial company that conforms to the definitions of the product mix. The authors’ motivation for this work is driven by firms’ desire to satisfy specific customer needs, and to respond to them quickly under uncertain demand. Our goal, using theories from finance, production management, and product offering management, is to conclude that there is a relevant difference between the evaluation of the technology that is to be chosen, and the potential value due to product mix adaptations that are able to provide the maximum return from investment. We address problems related to standard and customized production systems, and the decision to invest in a set of resources that will enable this choice.     

Effect of product mix on multi-product pull control

Product mix influences the performance of pull production control strategy in multi-product manufacturing systems. The complexity of product mix on the performance of a manufacturing system is primarily based on the characteristics of the demand and production control strategies. Demands are mainly characterised by volume and product-type while production control strategy is characterised by material release time, part flow, inventory control and throughput times. In multi-product systems, pull production control strategy operates dedicated or shared Kanban allocation policy. This paper examines the performance of the Generalised Kanban Control Strategy (GKCS), Extended Kanban Control Strategy (EKCS) and Basestock Kanban-CONWIP (BK-CONWIP) control strategy operating Shared Kanban Allocation Policies (S-KAP) or Dedicated Kanban Allocation Policies (D-KAP) for a healthcare parallel/serial assembly line with setup times. A simulation based multi-objective optimisation technique was adopted to examine the effect of different product mixes on the strategies and policies. A ranking and selection technique for multiple systems was used to screen the performance of the strategies. It was shown that product mix variability in a system influence the inventory levels of the pull control strategies examined. However, the performances of the strategies vary with strategies operating S-KAP having better inventory control than strategies operating D-KAP. Similarly, BK-CONWIP outperformed its alternatives.     

A decision support tool to facilitate the design of cellular manufacturing layouts

This paper presents a decision support tool that can be used by practitioners and industrialists to solve practical cell formation problems. The tool is based on a cell formation algorithm that employs a set of heuristic rules to obtain a quasi-optimal solution from both component routing information and other significant production data. The algorithm has been tested on a number of data sets obtained from the literature. The test results have demonstrated that in many cases the algorithm has produced an exceptional performance in terms of the grouping efficiency, grouping efficacy and quality index measures. The algorithm, to an extent, overcomes common problems in existing cell formation methods such as in dealing with ill-structured matrices and achieving rational cell sizes.     

A decision support system for cellular manufacturing system design

With the growth of competitive pressure in the global markets, there has been an increase in demand in industry for cellular manufacturing systems (CMSs) in order to improve productivity and process flexibility. The design of CMSs for industrial applications is a complex and knowledge intensive process as it involves the consideration of many factors including production data and process characteristics. This paper describes the development and implementation of a decision support system for the feasibility and conceptual design of CMSs. The system is based on the knowledge-based system approach, and is able to make recommendations of system feasibility, cell formation techniques and cell types. A case study is also presented to demonstrate the capability of the decision support system.     

A self-organizing neural network approach for the design of cellular manufacturing systems

The crux problem of group technology (GT) is the identification of part families requiring similar manufacturing processes and the rearrangement of machines to minimize the number of parts that visit more than one machine cell. This paper presents an improved method for part family formation, machine cell identification, bottleneck machine detection and the natural cluster generation using a self-organizing neural network. In addition, the generalization ability of the neural network makes it possible to assign the new parts to the existing machine cells without repeating the entire computational process. A computer program is developed to illustrate the effectiveness of this heuristic method by comparing it with the optimal technique for large-scale problems.     

A methodology to redesign heterogeneous product portfolios as homogeneous product families

This paper presents a methodology to redesign heterogeneous product portfolios as homogeneous product families. A heterogeneous portfolio is one that has no common components, or shares a minimum number of components. The methodology proposed to homogenize the portfolio begins by analyzing the current product offerings to determine customer needs and functions. Then, a granulation process is initiated to remove redundancies, and identify physical function carriers to deliver the required functions. Next, function carriers are grouped on the basis of their functional commonalities into groups for parallel development. The applicability and the use of the methodology are illustrated through the redesign of an existing heterogeneous ‘office furniture’ product portfolio.     

Comparison of visualization of optimal clustering using self-organizing map and growing hierarchical self-organizing map in cellular manufacturing system

The present research deals with the cell formation problem (CFP) of cellular manufacturing system which is a NP-hard problem thus, the development of optimum machine-part cell formation algorithms has always been the primary attraction in the design of cellular manufacturing system. In this proposed work, the self-organizing map (SOM) approach has been used which is able to project data from a high-dimensional space to a low-dimensional space so it is considered a visualized approach for explaining a complicated CFP data set. However, for a large data set with a high dimensionality, a traditional flat SOM seems difficult to further explain the concepts inside the clusters. We propose one such possible solution for a large CFP data set by using the SOM in a hierarchical manner known as growing hierarchical self-organizing map (GHSOM). In the present work, the two novel contributions using GHSOM are: the choice of optimum architecture through the minimum pattern units extracted at layer 1 for the respective threshold values and selection. Furthermore, the experimental results clearly indicated that the machine-part visual clustering using GHSOM can be successfully applied in identifying a cohesive set of part family that is processed by a machine group. Computational experience specifically with the proposed GHSOM algorithm, on a set of 15 CFP problems from the literature, has shown that it performs remarkably well. The GHSOM algorithm obtained solutions that are at least as good as the ones found the literature. For 75% of the cell formation problems, the GHSOM algorithm improved the goodness of cell formation through GTE performance measure using SOM as well as best one from the literature, in some cases by as much as more than 12.81% (GTE). Thus, comparing the results of the experiment in this paper with the SOM and GHSOM using the paired t-test it has been revealed that the GHSOM approach performed better than the SOM approach so far the group technology efficiency (GTE) measures of performance of the goodness of cell formation is concerned.     

Sustainable product development and manufacturing by considering environmental requirements

Sustainability in the development and manufacture of new products is a strategy that is widely accepted in principle, although not yet widely practiced. The integration of environmental requirements throughout the entire lifetime of a product needs a new way of thinking and new decision tools to be applied. This paper describes the concept of an approach to product development, based on a paradigm for sustainable manufacturing. Four examples of methodologies and decision tools are discussed, representing the most important sources of environmental impacts of a product. The emphasis lies on the integrating concept rather than on the details of the methodologies.     

Capacity utilization and machine sharing in a group technology based manufacturing system

Each cell in a group technology-based manufacturing system can be assumed as being comprised of two categories of machine types: (a) Dedicated to the cell, (b) Shared by other cells. The shared machine types constitute the major problem in the implementation of a cellular manufacturing system. This paper demonstrates, using a hypothetical case study, a mathematical programming formulation that exploits the existence of shared machine types in a cellular manufacturing system. Part transfer for the parts whose output levels are restricted by available capacity or shared machine types can be found which will minimize the material handling penalties.     

Applying simulated annealing for designing cellular manufacturing systems using MDmTSP

The design of cellular manufacturing systems involves many structural and operational issues. One of the important design steps is the formation of part families and machine cells (cell formation). Despite a large number of papers on cell formation published worldwide, only a handful incorporates operation sequence in layout design (intra-cell move calculations). We propose a solution to solve the part-family and machine-cell formation problem considering the within-cell layout problem, simultaneously. In this paper, the cellular manufacturing system is formulated as a multiple departures single destination multiple travelling salesman problem (MDmTSP) and a solution methodology based on simulated annealing is proposed to solve the formulated model. Numerical examples show that the proposed method is efficient and effective in finding optimal solutions. The results also indicate that the proposed approach performs well compared to some well-known cell formation methods.     

Formation of machine cells and part families in cellular manufacturing systems using a linear assignment algorithm

This paper presents a linear assignment algorithm for machine-cell and part-family formation for the design of cellular manufacturing systems. The present approach begins with the determination of part-family or machine-cell representatives by means of comparing similarity coefficients between parts or machines and finding a set of the least similar parts or machines. Using the group representatives and associated similarity coefficients, a linear assignment model is formulated for solving the formation problem by allocating the remaining parts or machines and maximizing a similarity index. Based on the formulated linear assignment model, a group formation algorithm is developed. The results of a comparative study based on multiple performance criteria and many existing data sets show that the present approach is very effective and efficient, especially in dealing with large-sized problems.     

Applications of group technology in distributed manufacturing

This paper discusses the systematic use of group technology (GT) to support important activities of distributed manufacturing including design indexing and retrieval, variant design, variant process planning, and design critiquing. We introduce an Object-Oriented Group Technology (OOGT) scheme, which captures design data critical to the product's manufacture. Our approach uses a STEP-based product information model to generate the OOGT data automatically. The latter are then used to conduct an efficient search for similar products manufactured by selected companies and to retrieve and use information about the manufacturing processes and performance of these similar products.     

A methodology for evolutionary product design

this paper describes a function-based approach for conceptual design support in the context of evolutionary product development. The main objective is to improve a designers productivity by the effective reuse of existing design information in design alternative identification, evaluation, and modification. An integrated evolutionary design methodology, EPD, is presented. The proposed methodology divides the whole process into three inter-related phases: information recovery, information management, and information reuse. The detailed steps in each phase are elaborated, and various techniques are employed to improve information reuse efficiency. A case study on commercial electrostatic air cleaner was used to illustrate the whole process of product evolutionary design. The proposed methodology will have a positive impact on the future development of the conceptual design support system.     

A new mathematical model for integrating all incidence matrices in multi-dimensional cellular manufacturing system

In the past several years, many studies have been carried out on cellular manufacturing based on a two-dimensional machine–part incidence matrix. Since workers have important role in doing jobs on machines, assignment of workers to cells becomes a crucial factor for fully utilization of cellular manufacturing systems. In this paper, an attempt is made to solve cell formation problem and minimize the number of voids and exceptional elements in a three dimensional (cubic) machine–part–worker incidence matrix. The proposed mathematical model captures the capability of workers in doing different jobs. To demonstrate the effectiveness of the proposed model, the solution of some test problems is compared with the literature method.     

A methodology to design virtual cellular manufacturing systems

Virtual Cellular Manufacturing System is a new type of cellular system that emerged as a promising alternative for designing cells in real time. The problem is complex and time consuming NP-hard problem in its nature. This study was conducted to design virtual cells that maximizes similarity index and minimizes lead time. A mathematical model and an effective solution procedure were proposed. Several randomly generated data sets were used along a case study to test the performance of the proposed approach. The effects of inherent parameters of virtual systems were investigated and compared by using particular performance measures.     

Neural network-based design of cellular manufacturing systems

A neural network based on a competitive learning rule, when trained with the part machine incidence matrix of a large number of parts, classifies the parts and machines into part families and machine cells, respectively. This classification compares well with the classical clustering techniques. The steady state values of the activations and interconnecting strengths enable easier identification of the part families, machine cells, overlapping parts and bottleneck machines. Neural networks are mostly applied by treating them as a blackbox, i.e. the interaction with the environment and the information acquisition and retrieval occurs at the input and the output level of the network. This paper presents an approach where knowledge is extracted from the external and internal structure of the neural network.     

The canonical expression of the drama product manufacturing processes

As the broadcast industry is evolving toward IT-based facilities, the production workflows and their associated production metadata should similarly take advantage of IT commodities. This paper presents a manufacturing system for the production of drama television and motion picture programmes, constructed using IT-based technologies in a file-based media environment. This drama production facility implements a production workflow based on common industrial manufacturing processes and extensively models the individual aspects of the drama production process. We aim to show that the different processes contained in this manufacturing workflow can be expressed in terms of elementary building blocks, the canonical processes of media production. By identifying recurring and canonical functionality, process implementations can be simplified and input and output from different processes can be coordinated for better integration with external systems.     

Stochastic cellular manufacturing system design subject to maximum acceptable risk level

In this study, a non-linear mathematical model is proposed to solve the stochastic cellular manufacturing system (CMS) design problem. The problem is observed in both machine and labor-intensive cells, where operation times are probabilistic in addition to uncertain customer demand. We assume that processing times and customer demand are normally distributed. The objective is to design a CMS with product families that are formed with most similar products and minimum number of cells and machines for a specified risk level. Various experiments are carried out to study the impact of risk level on CMS design. As the risk level increases, lower number of cells and product families are formed and average cell utilization increases. However, this leads to poor performance in cells, where standard deviations of capacity requirements are high. Later, the deterministic approach proposed by Suer, Huang, and Sripathi (2010) and the proposed stochastic model with various risk levels are compared. Both of the models’ results are simulated with Arena Simulation Software. Simulation is performed to validate models and assess the performance of designed CMSs with respect to following measures: cell utilization, WIP, total waiting time and total number waiting. Stochastic CMS design with 10% risk formed a better CMS in all of the performance measures according to the results obtained from simulation experiments.