Machine Grouping Problem Research Articles

A Critical Review of Machine Loading Problem in Flexible Manufacturing System

Production planning is the foremost task for manufacturing firms to deal with, especially adopting Flexible Manufacturing System (FMS) as the manufacturing strategy for production seeking an optimal balance between productivity-flexibility requirements. Production planning in FMS provides a solution to problems regarding part type selection: machine grouping, production ratio, resource allocation and loading problem. These problems need to be solved optimally for maximum utilization of resources. Optimal solution to these problems has been a focus of attention in production and manufacturing, industrial and academic research since a number of decades. Evolution of new optimization techniques, software, technology, machines and computer languages provides the scope of a better optimal solution to the existing problems. Thus there remains a need of research to solve the problem with latest tools and techniques for higher optimal use of available resources. As an objective, the researchers need to reduce the computational time and cost, complexity of the problem, solution approach viz. general or customized, better user friendly communication with machine, higher freedom to select the desired objective(s) type(s) for optimal solution to the problem. As an approach to the solution to the problem, a researcher first needs to go for an exhaustive literature review, where the researcher needs to find the research gaps, compare and analyze the tools and techniques used, number of objectives considered for optimization and need, and scope of research for the research problem. The present study is a review paper analyzing the research gaps, approach and techniques used, scope of new optimization techniques or any other research, objectives considered and validation approaches for loading problems of production planning in FMS.

An algorithm for part families identification in cellular manufacturing

The formation of machine–part families is an important task in the design of cellular and flexible manufacturing systems. The formation results in the creation of many benefits for manufacturing systems. Among the many methods utilised in machine–cells formation, the similarity coefficient method (SCM) is most widely used. When SCM is used, the rearrangement of machine and part components is required to form machine–part families. This process of rearrangement has been considered as being subjective and difficult, may result in improper assignments to parts families, resulting in a negation of benefits promised. This paper presents an effective algorithm to identify part–families and bottleneck parts, given machine groupings, rather than addressing the machine grouping problem in general.

Fuzzy ART K-Means Clustering Technique: a hybrid neural network approach to cellularmanufacturing systems

Cellular manufacturing system (CMS) is regarded as an efficient production strategy for batch type of production. Literature suggests, since the last two decades neural network has been intensively used in cell formation while production factor such as operation time is merely considered. This paper presents a new hybrid neural network approach, Fuzzy ART K-Means Clustering Technique (FAKMCT), to solve the part machine grouping problem in CMS considering operation time. The performance of the proposed technique is tested with problems from open literature and the results are compared to the existing clustering models such as simple K-means algorithm and modified ART1 algorithm as found in the recent literature. The results support the better performance of the proposed algorithm. The novelty of this study lies in the simple and efficient methodology to produce quick solutions with least computational efforts.

Collaborative particle swarm optimization with a data mining technique for manufacturing cell design

In recent years, different metaheuristic methods have been used to solve clustering problems. This paper addresses the problem of manufacturing cell formation using a modified particle swarm optimization (PSO) algorithm. The main modification that this work made to the original PSO algorithm consists in not using the vector of velocities that the standard PSO algorithm does. The proposed algorithm uses the concept of proportional likelihood with modifications, a technique that is used in data mining applications. Some simulation results are presented and compared with results from literature. The criterion used to group the machines into cells is based on the minimization of intercell movements. The computational results show that the PSO algorithm is able to find the optimal solutions in almost all instances, and its use in machine grouping problems is feasible.

Comparative study of simulated annealing, genetic algorithms and tabu search for solving binary and comprehensive machine-grouping problems

Machine/part grouping problems have proven to be NP-complete and cannot be solved in polynomial time. Solving such problems of reasonable size often relies on heuristic approaches. Recently, several metaheuristic approaches have emerged as efficient tools for solving such problems. However, the development and implementation of such meta-heuristics have not been a trivial issue. The merits of each method and the problems involved in implementation may not be easily apprehended by practitioners, thereby posing difficulties in the selection of an efficient heuristic for industrial applications. For this reason, a comparative study of three important metaheuristic approaches--simulated annealing, genetic algorithms and tabu search for both binary (considering only machines and part types) and comprehensive (involving machine/part types, processing times, lot sizes, and machine capacities) machine grouping problems--was carried out. To test the performance of the three metaheuristics, two binary performance indices and two generalized performance indices were respectively used for binary and comprehensive machine/part grouping problems. The comparisons were made in terms of solution quality, search convergence behaviour and presearch effort. The results indicate that simulated annealing outperforms both genetic algorithm and tabu search particularly for large problems. The genetic algorithm seems slightly better than the tabu search method for the comprehensive grouping problems.

The Application of Multiparadigm Simulation Techniques to Manufacturing Processes

The simulation of manufacturing processes and manufacturing systems using computers has been carried out for several decades. The use of discrete event simulation models for production systems, material planning, machine grouping and various other key problems of resource allocation have been well researched and a number of ready to use commercial products are available. In recent years, the use of artificial intelligence simulation and modelling techniques for certain manufacturing processes have led to significant advantages over conventional techniques. However, the benefits of using AI techniques have not been fully exploited in existing simulation systems. Owing to the different way that some AI techniques process or represent simulation data compared to conventional mathematically based methods, it is not always easy to include AI techniques in existing simulation systems with their inherent structures for representing simulation data. In this paper, a concept which allows the use of AI techniques such as artificial neural networks, genetic algorithms and fuzzy sets as well as traditional mathematical techniques for the simulation and modelling of certain manufacturing processes is presented and discussed. Methods for the exchange of simulation data are demonstrated using the milling process as an example. Furthermore, the notion of distributed simulation and the determination of essential process parameters by remote access to common simulation databases using a client/server model has been reviewed.

Identifications of part families and bottleneck parts in cellular manufacturing

The formation of machine‐part families is an important task in the design of cellular and flexible manufacturing systems. The formation results in the creation of many benefits for manufacturing systems. Among the many methods utilized in machine‐cells formation, the similarity coefficient method (SCM) is most widely used. When SCM is used, the rearrangement of machine and part components is required to form machine‐part families. This process of rearrangement has been considered as being subjective and difficult and may result in improper assignments to parts families, resulting in a negation of benefits promised. This paper presents an effective algorithm to identify part‐families and bottleneck‐parts, given machine groupings, rather than addressing the machine grouping problem in general.

Impact of throughput-based objectives and machine grouping decisions on the short-term performance of flexible manufacturing systems

The most commonly-used objective in the literature for solving machine loading and grouping problems in flexible manufacturing systems is the maximization of steady-state throughput. In reality, orders arrive dynamically and the mix of products changes frequently, raising questions about the applicability of this objective in achieving shorter-term measures of the output rate. Moreover, the loading and grouping problems only determine which operations are to be performed on each machine, and whether any of the machines should be identically tooled so as to allow multiple routeings for some jobs. The actual short-term performance of the system also depends on scheduling and dispatching decisions. We study the impact of the various objectives ostensibly related to steady-state throughput and machine grouping decisions on the short-term makespan performance (in a static setting). Computational results suggest that minimizing the maximum percentage overload (relative to the optimal continuous workload allocation) across machine groups is an excellent objective. The results also indicate that reducing the number of machine groups and balancing workloads among the machines help to reduce makespan.

An objective-guided ortho-synapse Hopfield network approach to machine grouping problems

This paper reports an ortho-synapse Hopfield network (OSHN) for solving machine grouping problems. An objective-guided search scheme is proposed to lead the search process. The OSHN structure and the objective-guided search scheme have been implemented in an algorithm. Unlike the back propagation or A RT1 neural networks, the proposed approach does not require training process and is not affected by the initial arrangement of the machine-part incidence matrix. As compared with the original Hopfield neural network, the computational efficiency and the solution quality can be considerably improved due to the reduced synapses in the OSHN and the objective-guided search process. The performance of the proposed algorithm has been tested using 28 notable problems from the literature and compared favourably with the solutions obtained in the literature.

Grouping and placement of machine cells

We consider a real-world machine grouping and layout problem in which the objective is not only to identify machine cells and corresponding part families but also to determine a near-optimal layout of machines within each cell and the cells themselves. The three-stage approach developed incorporates materials flow considerations while determining machine groups, considers alternative process plans, exploits part routing information and allows the user to perform sensitivity analysis. The menu-driven, interactive program has been applied to four test problems and an industrial problem. Our experience with applying the approach to the industrial problem is also reported.

A pragmatic approach to machine and part grouping in cellular manufacturing system design

The paper presents an easy-to-use decision support tool for the designer of manufacturing systems who intends to implement Group Technology (GT) concepts. In related literature two main research schools can be identified: one considers conversion to cellular manufacturing as a compulsory step toward efficiency, whose benefits are quite independent from the solution approach; the other looks instead for optimal solutions by using operational research models. The proposed approach may be located halfway between the two: at first a multicriteria model evaluates the suitability of cellular manufacturing and of some of its variants with respect to the company's environment; in the second step a heuristic solution to the part and machine grouping problem is generated, taking a wide set of factors into account (safety, technological, organizational…). The tool has then been tested on an industrial case in order to prove its suitability for supporting the designer's decision process.

Grouping and placement of machine cells

We consider a real-world machine grouping and layout problem in which the objective is not only to identify machine cells and corresponding part families but also to determine a near-optimal layout of machines within each cell and the cells themselves. The three-stage approach developed incorporates materials flow considerations while determining machine groups, considers alternative process plans, exploits part routing information and allows the user to perform sensitivity analysis. The menu-driven, interactive program has been applied to four test problems and an industrial problem. Our experience with applying the approach to the industrial problem is also reported.

A genetic-algorithm-based heuristic for the GT cell formation problem

This paper presents a heuristic for the machine-part grouping problem which incorporates relevant production requirements such as routing sequence, production volume, unit handling size, unit processing time and cell size. The heuristic consists of two phases. The first phase is developed based on a genetic algorithm and greedy heuristic to solve the machine grouping problem. Once machine cells are identified, the second phase is employed to identify the associated part families. The performance of the heuristic is examined through a comparative study with some existing solution methods. Global efficiency, group efficiency, intercell move factor and grouping effectiveness are adopted as comparative measures.

DESIGNING CELLULAR MANUFACTURING SYSTEMS: BRANCH-AND-BOUND AND A∗ APPROACHES

In this paper, heuristic and optimal algorithms for solving the group technology problem are presented. The heuristic algorithm is based on a branch-and-bound concept. A quadratic programming model for the machine grouping problem is formulated. The A∗ algorithm is developed for optimal solving of the machine grouping problem. The performance of the heuristic branch-and-bound method and the A∗ algorithm is compared with several existing heuristics.

Building a short-term production planning system for flexible manufacturing systems: an integration viewpoint

Abstract From the viewpoint of integration, the production objective of flexible manufacturing systems lFMSr should follow results of MRPsCRP. Thus, the planning goal of short-term production planning lSTPPr is to satisfy quantity and due date requirements determined by MRPsCRP. The STPP of FMS should cover part type selection, production ratio, process selection, process ratio, resource allocation, machine grouping, routing mix and loading problems. A STPP system is built to solve the STPP problem of an FMS having multiple machine types, each with multiple m.'ichinc units. This system adopts a variable-period flexible planning approach, using part mix and process selection flexibilities to solve STPP problems. It also provides flexibilities in its operations sequence and in the machine routing to the dynamic operation planning system.

Integrating the grouping and layout problems in cellular manufacturing systems

In this paper, the simultaneous solution of the machine grouping and layout problems in cellular manufacturing systems is explored. A model for the combined problem is presented. Since the model is complex to solve using traditional optimization techniques, a suboptimal procedure involving the use of a simulated annealing based algorithm is suggested. Results with a numerical example are presented. An alternative formulation based on quandratic assignment problem is also presented.

Capacity selection by mathematical programming in cellular manufacturing systems

Abstract Determining the types of machines needed for a production process and choosing the number of each type to meet demand are important manufacturing and production planning decisions with both strategic and tactical implications. Determining the types of machines is essentially a process selection problem. Given that choice, choosing the number of each type of machine is a capacity planning problem. However, it has not so far received sufficient attention in the area of cellular manufacturing systems. By relaxing the requirement that ail operations for any given part be performed in a single cell, one can trade off material handling costs against capital costs to arrive at simultaneous decisions about machine capacity and machine grouping. This paper formulates a quadratic integer programming model of the capacity selection and machine grouping problems.

Machine grouping problem in cellular manufacturing systems— an integer programming approach

In this paper a methodology is proposed to group the machines in cellular manufacturing systems based on the tooling requirements of the parts, toolings available on the machines and the processing times. Two 0-1 integer programming formulations are proposed. These formulations assume that the part families are known. The first formulation groups the machines based on the compatibility of parts with machines. The second formulation groups the machines in order to minimise the cost of allocating the machines and the cost of intercell movement. These formulations take into account the limitations on the number of machines in a group and the number of machines available of a particular type. The application of these formulations is illustrated using an example.

Knowledge based approach to machine cell layout

In this paper, the machine cell layout problem is examined. A new methodology for solving the problem is proposed. The methodology involves three stages. In the first stage, an algorithm suitable for solving the machine grouping problem is utilized. In the second and third stages, mathematical programming models of the machine cell and machine layout problems are formulated and solved using suitable algorithms. The development of a knowledge based system which uses models and algorithms for solving the machine grouping and layout problems, is also outlined.

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.