Comparison of layered cellular manufacturing system design approaches

In this study, a mathematical programming approach is proposed to design a layered cellular manufacturing system in highly fluctuated demand environment. A mathematical model is developed to create dedicated, shared and remainder cells with the objective of minimizing the number of cells. In contrast with classical cellular manufacturing systems, in layered cellular systems, some cells can serve to multiple part families. A five-step hierarchical methodology is employed: (1) formation of part families, (2) calculation of expected cell utilizations and demand coverage probabilities, (3) specification cell types as dedicated, shared, and remainder cells, (4) simulation of proposed layered systems to evaluate their performance with respect to average flowtime and work-in-process inventory, and (5) statistical analysis to find the best layered cellular design among alternatives. It is found that designs with higher number of part families tend to have less number of machines. Similar results are also observed with respect to average flowtime and work-in-process inventory measures. The results are also compared with a heuristic approach from the literature. None of the approaches is dominant with respect to all of the performance measures. Mathematical modeling approach performs better in terms of number of machines for most of the alternative designs. However, heuristic approach yields better average flowtime and work-in-process inventory for most of the designs.     

A study of labor assignment flexibility in cellular manufacturing systems

The objective of this research is to study labor flexibility in cellular manufacturing systems characterized by intra-cell operator's mobility. The special focus of the investigation is to explore the impact that using different labor allocation strategies have on system performance. This internal aspect of labor flexibility is referred to as labor assignment flexibility. Labor strategies are classified according to the type of machine-operator assignments including dedicated (when only one operator is responsible for a machine or group or machines), shared (when more than one operator is responsible for a machine or a group of machines) or combined assignments (when the operator has both dedicated and shared machine assignments). This work proposes a classification scheme and a framework that is composed by a set of propositions that evolved from an empirical study and includes the concepts of workload balancing, workload sharing, and the presence of bottleneck operations. The suitability of the framework is tested using simulation modeling in an actual cell implementation. The experimental results based on labor strategies using two and three operators show that the balance in the workload assigned to the individual operators and the level of shared workload are significant factors in determining the performance of the system.     

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 methodology to incorporate product mix variations in cellular manufacturing

The identification of part families and machine groups that form the cells is a major step in the development of a cellular manufacturing system and, consequently, a large number of concepts, theories and algorithms have been proposed. One common assumption for most of these cell formation algorithms is that the product mix remains stable over a period of time. In today’s world, the market demand is being shaped by consumers resulting in a highly volatile market. This has given rise to a new class of products characterized by low volume and high variety. To incorporate product mix changes into an existing cellular manufacturing system many important issues have to be tackled. In this paper, a methodology to incorporate new parts and machines into an existing cellular manufacturing system has been presented. The objective is to fit the new parts and machines into an existing cellular manufacturing system thereby increasing machine utilization and reducing investment in new equipment.     

A simulation based experimental design to analyze factors affecting production flow time

In this paper we analyze and evaluate the effects of some pre-defined process parameters on the performance of a manufacturing system. These parameters include two different plant layout types, namely functional layout (FL) and cellular manufacturing layout (CL), as well as scheduling rule, machine downtimes, batch sizes, and transporter (interstage transporters) capacities. First we employ simulation to evaluate the effects of these factors on the performance of the system and then conduct designed experiments to set the best levels for these factors. The performance evaluation function is defined in terms of the average flow time of all the part types through the manufacturing system. Arena 10.0 simulation software and SPSS 9.0 statistical package are used to measure the main effects and interactions between these factors. This work demonstrates that various manufacturing parameters should be considered jointly when designing or redesigning a facility because setting different levels for parameters can considerably affect the performance of a facility.     

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.     

Recent trends in flexible automated manufacturing

In recent years, an important change in developing and implementing manufacturing systems can be observed in all industrialized countries. Due to intense international competition and the growth of personnel costs, companies are forced to use systems with a higher level of automation. At the same time, an increasing number of product variation—caused by adapting the products to customer requirements—demand more flexibility of machine tools and manufacturing systems.Basic types of computer-controlled flexible automated manufacturing systems will be shown and characterized by their special ranges of application. A planning procedure is presented which includes selecting a suitable systems structure such as; transfer line or flexible manufacturing system or automated manufacturing cell, and for determinating every single machine tool within the system. Examples of several new computer-controlled manufacturing systems which have been planned by the above planning procedure are presented with special regard to the evaluation of the system's economic performance.Finally, some rules are given for measuring the economic performance of automated manufacturing or assembly systems including automation of the handling functions.     

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.     

Design of cellular manufacturing systems—An industrial case study

This paper discusses the development of a cellular manufacturing system in a manufacturing company. A new clustering algorithm has been applied to the design of the system. The algorithm consists of two parts, a cluster-seeking process and the minimization of bottleneck machines. Two parameters are input by the user: (a) the desired number of machine cells or part families, and (b) the minimum number of parts within each cell or part family. A number of cells have been designed for a preparation shop, a fabrication shop, and a machine shop. Both the advantages and disadvantages of the algorithm have been analyzed, especially concerning its applications to industry.     

An ACO-based intercell scheduling approach for job shop cells with multiple single processing machines and one batch processing machine

Intercell transfers are inevitable for the manufacturing of complicated products, which disrupts the philosophy of cellular manufacturing and leads new challenges to the field of production scheduling. The issue of intercell scheduling is analyzed in the context of a cellular manufacturing system consisting of multiple single-processing machines and one batch-processing machine, which is derived from the actual manufacturing of complicated assemblies in the equipment manufacturing industry. Since the two types of machines are different from, even contrary to, each other in some constraint conditions, a combinational ant colony optimization (CACO) approach is developed in this paper, which designs two structures for the single-processing machines and the batch-processing machine, respectively. By updating pheromone trails integratedly and scheduling the single-processing operations and the batch-processing operations simultaneously, cooperative optimization for the two types of machines is achieved in the CACO. Minimizing the maximum completion time is taken as the scheduling objectives. Computational results show that the CACO has significant advantages comparing with other approaches and the CPLEX, and is especially suitable for the large dimension problems.     

Performance analysis of machine cell configurations using simulation

The objective of this research is to develop a generalized model that can analyze the performance of any cellular manufacturing system under various combinations of dispatching and loading policies. Early stages of the research along this line before generalization of the model is presented in this paper. A menu-driven SIMAN based simulation model is developed to analyze the performance of a particular cellular manufacturing system in terms of machine utilization, tool utilization, transporter (AGV) utilization, part waiting time, etc.     

A Human‐Oriented Simulation Approach for Labor Assignment Flexibility in Changeover Processes of Manufacturing Cells

Skilled operators are the most decisive key factors in manufacturing cells. An optimal assignment of operators is crucial for flexibility and productivity. Although there are many publications dealing with labor assignment problems, different forms of human cooperation on the shop floor and decentralized decision making, which are the main factors for system flexibility, are seldom concerned in existing models. In this article, a human‐oriented methodology to analyze, simulate, and evaluate labor assignment flexibility in changeover processes in manufacturing cells is introduced, which is characterized by an agent‐based approach. First, the problem architecture is presented along with the concepts of labor flexibility. Then, different types of human behavior in the changeover process are modeled. Furthermore, a human–machine interaction model is developed to integrate the human agent models into a generalized discrete event dynamic system (DEDS) process model. In this way, work process dynamics and cooperative behavior can be explicitly modeled and simulated. Third, the model is verified on the basis of a motorcycle engine manufacturing cell, and simulation experiments with different labor assignment schemes are designed and conducted. The simulation results show that assignment strategies incorporating different skill levels and cooperation styles have a significant impact on system performance. The agent‐based approach in conjunction with the human–machine interaction model can be used to analyze and solve a large class of assignment problems in flexible manufacturing systems, especially when human cooperation and collaboration are key factors shaping overall system performance.     

Forming part families by using genetic algorithm and designing machine cells under demand changes

This study develops a methodology which can be used to form manufacturing cells using both a new similarity coefficient based on the number of alternative routes during machine failure and demand changes for multiple periods. The methodology is divided into two phases. A new similarity coefficient, which considers the number of available alternative routes when available during machine failure, is suggested in Phase I. The primary objective of Phase I is to identify part families based on the new similarity coefficient by using a genetic algorithm. One of the major factors contributing to the success of cell implementation is flexibility for demand changes. It is difficult to reorganize the cells according to changes in demand, available machine capacity, and due date. Most of the suggested approaches in the literature tend to use a fixed demand for cellular manufacturing systems. Due to demand changes, cell design should include more than the one period that most researchers of cellular manufacturing systems consider. A new methodology for cell formation, which considers the scheduling and operational aspects in cell design under demand changes, is introduced in Phase II. Machines are assigned to part families by using an optimization technique. This optimization technique employs sequential and simultaneous mixed integer programming models for a given period to minimize the total costs which are related to the scheduling and operational aspects.     

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.     

Using multi-agent architecture in FMS for dynamic scheduling

The proposed scheduling strategy is based on a multi-agent architecture. Each agent of this architecture is dedicated to a work centre (i.e. a set of resources of the manufacturing system); it selects locally and dynamically the most suitable dispatching rules. Depending on local and global considerations, a new selection is carried out each time a predefined event occurs (for example, a machine becomes available, or a machine breaks down). The selection depends on: (1) primary and secondary performance objectives, (2) the operating conditions, and (3) an analysis of the system state, which aims to detect particular symptoms from the values of certain system variables. We explain how the scheduling strategy is shared out between agents, how each agent performs a local dynamic scheduling by selecting an adequate dispatching rule, and how agents can coordinate their actions to perform a global dynamic scheduling of the manufacturing system. Each agent can be implemented through object-oriented formalisms. The selection method is improved through the optimization of the numerical thresholds used in the detection of symptoms. This approach is compared with the use of SPT, SIX, MOD, CEXSPT and CR/SPT on a jobshop problem, already used in other research works. The results indicate significant improvements.     

A simulation study of hierarchical clustering techniques for the design of cellular manufacturing systems

Forming part families and their associated machine groups remains one of the major concerns for companies changing from traditional batch manufacturing to cellular manufacturing. Cluster analysis is recognized as an appropriate procedure for forming the machine cells and part families necessary for the design of a cellular manufacturing system. In this paper, our concern is with using computer simulation to evaluate cluster analysis as a tool for designing cellular systems.     

Cell control system

FANUC has recently developed a machining cell capable of a wide range of manufacturing activities.

This machining cell consists of a Computer Numerically Controlled (CNC) machine tool, an industrial robot and monitoring units. Any number of these cells can be grouped together in a factory to create a highly effective manufacturing system.

This paper outlines the Cell Control System using distributed control and integrated processing.     

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.     

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.