Machine-part Incidence Matrix Research Articles

Re-Layout Tata Letak Bagian Percetakan Menggunakan Calullar Manufacturing System

Material handling in the production division of publisher X encountered several obstacles, including the transfer of material between production processes having cross-movement flows. This condition occurs as a result of an irregular engine layout. The impact of the conditions in the transfer of material in the production process is the distance of the transfer to be 968.6 m/day. This condition causes some machines to be repositioned, so that material transfer can run faster and more efficiently. One of the efforts to reduce material transfer is to re-plan the placement of production facilities by adopting the concept of a cellular manufacturing system or cellular manufacturing system that is feasible to be applied to the production process of publisher X. The cellular manufacturing system was chosen because it is in accordance with the number of characteristics of the publishing industry that requires the ability to anticipate changes. This rapid demand suits a particular market. This article describes how the results of rearranging and minimizing the distance of moving raw materials in the production section of publisher X by adopting the concept of a cellular manufacturing system and rearranging the layout of production facilities by using sorting using Sorting Algorithms. The selection of the Sorting Algorithms is based on consideration of the order of the output which is not coherent, so it needs to be sorted. Efficient sorting is needed to determine the part-machine incidence matrix which is then used in the development of cellular manufacturing systems. Program development on Visual Studio 2012 software shows the results of facility layout planning by grouping 20 machines and 11 products with this layout change, the total material handling distance is reduced by 371.8 m or by 61.61%

Simulated annealing and artificial immune system algorithms for cell formation with part family clustering

Cell formation problem (CFP) is one of the main problems in cellular manufacturing systems. Minimizing exceptional elements and voids is one of the common objectives in the CFP. The purpose of the present study is to propose a new model for cellular manufacturing systems to group parts and machines in dedicated cells using a part-machine incidence matrix to minimize the voids. After identifying the exceptional elements, the machines required for processing the remained operations of corresponding parts which are not processed in the dedicated cells are specified. This results in a new matrix called part family-machine. Then, by clustering the part family-machine incidence matrix, the part families that should be assigned to a specific cell to achieve the highest similarity can be determined. The similarity can be translated to sharing machines required for completing the processes and form new cells called shared cells to minimize the number of exceptional elements and voids. Unlike previous models in which the similarity is considered only in the dedicated cells, in the proposed model, the similarity would be monitored and observed in the entire production process. Due to the complexity of our model, two meta-heuristic algorithms including artificial immune system (AIS) and simulated annealing (SA) are proposed. The efficiency of the algorithms is compared to that of exact solutions. Also, the algorithms are compared regarding the quality of solutions. Finally, according to grouping efficacy measure, SA algorithm has a superior performance in comparison with AIS by spending less CPU time.

Cellular layout formation by using weighted similarity-based modified flow matrix with process sequence data

Cellular layout formation (CLF) is an important problem in the design of a cellular manufacturing system (CMS). From the previous studies, most of the researchers have used machine part incidence matrix (MPIM) as input in CMS. In this paper, weighted similarity based modified flow matrix is proposed to solve the cell formation problem (CFP) and CLF simultaneously. The objective of our proposed approach is to group dissimilar machines based on the process sequence of different parts. Two well-known benchmark problems are selected from the literature and results are compared based on modified grouping efficacy with the existing methods. The results clearly indicated that our proposed approach outperforms the previously proposed methods.

Cellular layout formation by using weighted similarity-based modified flow matrix with process sequence data

Cellular layout formation (CLF) is an important problem in the design of a cellular manufacturing system (CMS). From the previous studies, most of the researchers have used machine part incidence matrix (MPIM) as input in CMS. In this paper, weighted similarity based modified flow matrix is proposed to solve the cell formation problem (CFP) and CLF simultaneously. The objective of our proposed approach is to group dissimilar machines based on the process sequence of different parts. Two well-known benchmark problems are selected from the literature and results are compared based on modified grouping efficacy with the existing methods. The results clearly indicated that our proposed approach outperforms the previously proposed methods.

Partitioning of a manufacturing system into machine cells—a practical application

This article focuses on partitioning a manufacturing system into machine cells. The benefit of such a conversion is improving manufacturing efficiency by eliminating waste in terms of material handling and time as well as increasing production capacity of the manufacturing system. Rank order clustering (ROC) algorithm is used in this study for the conversion. It is easy to understand and its application in obtaining machine cells with a cellular layout is straightforward. ROC algorithm requires a part-machine incidence matrix. The matrix is rearranged until multiple part families and corresponding machine cells are obtained. MS Excel and MATLAB are used in the application. The study converts 44 machine manufacturing system into seven machine cells and brings important performance improvement.

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

Design of cellular manufacturing system for power press industry to reduce total travelling time by hybrid algorithm

Part family identification and machine cell formation are the two major steps in cellular manufacturing system. Cell formation is a complex process and there are several approaches for this purpose vis., array based clustering, agglomerative clustering, mathematical programming, graph partitioning and non-traditional methods. The problem with first two approaches is that the quality of the solution is dependent of the initial part machine incidence matrix. A new algorithm for cell formation combining the techniques of array based clustering and agglomerative clustering is proposed. The above proposed method is tested by using standard problems and compared with other method results for the same standard problems. Grouping efficiency is the most widely used measures of quality for cellular manufacturing systems. The proposed algorithm is used to form manufacturing cells in a power press industry to increase productivity by reducing total travelling time.

P-Median Approach for the Large-Size Multi-Objective Generalized Cell Formation

The p-median model is known as an effective mathematical model for small- to intermediate-size single- objective cell formation (SOCF) problem in the design of cellular manufacturing system. This paper shows that a sequential approach adopting the p-median model can be a very effective alternative for large-size multi-objective generalized cell formation (MOGCF). Our sequential approach proceeds through two-phase procedures. Phase 1 uses a comprehensive generalized part-machine incidence matrix (PMIM) incorporating realistic manufacturing factors such as alternative process plans and duplicate machines as well as processing times, operation sequences, nonconsecutive multiple visits to the same machines, and production volumes of parts. Based on a new generalized similarity coefficient, a 0-1 linear p-median model is formulated to form independent and compact machine cells. Phase 2 uses a systematic part assignment procedure based on a new classification scheme of part types with alternative process plans in pursuit of balanced workload among cells and high machine utilization. Phase 1 implemented using LINGO 16.0 reaches the global optimum within two seconds even on a large problem containing 1,600 binary variables and phase 2 completes speedy assignment of parts within two seconds with high-quality solutions.

Interactive design of cellular manufacturing systems, optimality and flexibility

Optimal design and flexible manufacturing are two objectives that have to be achieved in cellular manufacturing systems. Achieving both objectives will optimize the inter-cell movements of parts which will lead to a minimum number of voids and exceptions. The main problem in designing cellular manufacturing systems is cell formation. In the process of forming such manufacturing cells, the designer is faced with the constraint of limited number of cells, therefore the designer needs to specify the number of cells in advance. A number of heuristic methods have been developed in the literature to come up with acceptable but not optimal results due to the complexity of these optimization problems. In this paper a special case algorithm for generating a two-cell formation will be developed with unbounded cell size based on Stirling number. The aim of the proposed 2-cell algorithm is to obtain exact and optimal solutions, and to give flexibility to the system designer in order to decide and choose among alternative optimal solutions for machine-part incidence matrix. The superiority of the proposed algorithm for designing cellular manufacturing systems is to develop an approach that not only specifies the number of cells in advance but also determines all the possible ways to form p-cells from n-machines. To demonstrate the merits of the proposed 2-cell algorithm in designing machine-part matrix, the researcher borrows industrial problems cited in the literature to show that the developed algorithm offers designers better if not the same clusters as compared with known methods. The proposed approach is interactive in the sense that the designer can choose the suitable 2-cell design for a given scenario. Further, when the optimization routine is linked to automated cell layout software, the designer can interactively compare alternative cell designs and layouts and choose the most appropriate.

A metaheuristic approach for a cubic cell formation problem

The minimizations of voids and exceptional elements by considering the part-machine incidence matrix of the cell formation problem have been discussed in the literature. In recent years, a few mathematical models considering the assignment of workers to cells in addition to part and machine have been proposed to fully utilize manufacturing systems. Although the proposed mathematical models can produce the best solutions for small sized problems within reasonable times, they are inadequate to produce the best solutions for large sized real life cases due to the NP-hard nature of the problem. In this study, a genetic algorithm has been proposed for the problem of part-machine-worker cell formation. Furthermore, the Taguchi method, as a statistical optimization technique, has been used to determine the appropriate levels of the parameters. The performance of the proposed genetic algorithm has been tested using test data from the literature for small sized problems and using data that was generated in this study for large sized problems. The experimental results of this study show that the proposed genetic algorithm can produce the optimal solutions for small sized problems and that the proposed algorithm can yield optimal or near-optimal solutions for large sized problems within reasonable times.

New criteria for configuration of cellular manufacturing considering product mix variation

This paper deals with configuring manufacturing cells when product mix variation occurs. Most of researches have addressed the cell formation problem when part-machine incidence matrix is constant even for dynamic/stochastic case. But to the nature of CMS in manufacturing products in mid-variety and mid-volume, the product mix variation is not too far-fetched. Product mix variation causes the part-machine incidence matrix to change. To formulate the proposed problem two different criteria are considered which one relates to worker experts and another to worker relations. The first object considers the maximizing the expert levels in manufacturing cells. While the second object tries to maximize the interest levels in manufacturing cells. To make these concepts practical, a mathematical formulation which minimizes the voids of both worker-machine and worker-worker incidence matrices is developed. Due to the non-homogenous nature of the objective functions and possible conflicts, a bi-objective programming approach is applied. To find the Pareto-optimal front, the augmented ε-constraint method (AUGMECON) is applied. Since AUGMECON may not provide non-dominated set in a reasonable time, especially for large-size instances, NSGAII algorithm is customized and applied to produce optimal/near optimal Pareto solutions. To assess the performance of the proposed NSGAII algorithm, several randomly generated test problems were solved for a set of well-known multi-objective performance metrics.

Scalable co-Clustering using a Crossing Minimization ? Application to Production Flow Analysis

Production flow analysis includes various families of components and groups of machines. Machine-part cell formation means the optimal design of manufacturing cells consisting of similar machines producing similar products from a similar set of components. Most of the algorithms reorders of the machine-part incidence matrix. We generalize this classical concept to handle more than two elements of the production process (e.g. machine part product resource operator). The application of this extended concept requires an efficient optimization algorithm for the simultaneous grouping these elements. For this purpose, we propose a novel co-clustering technique based on crossing minimization of layered bipartite graphs. The present method has been implemented as a MATLAB toolbox. The efficiency of the proposed approach and developed tools is demonstrated by realistic case studies. The log-linear scalability of the algorithm is proven theoretically and experimentally.

An exact algorithm for maximizing grouping efficacy in part–machine clustering

The Grouping Efficacy Index (GEI) is well-recognized as a measure of the quality of a solution to a part–machine clustering problem. During the past two decades, numerous approximation procedures (heuristics and metaheuristics) have been proposed for maximization of the GEI. Although the development of effective approximation procedures is essential for large part–machine incidence matrices, the design of computationally feasible exact algorithms for modestly sized matrices also affords an important contribution. This article presents an exact (branch-and-bound) algorithm for maximization of the GEI. Among the important features of the algorithm are (i) the use of a relocation heuristic to establish a good lower bound for the GEI; (ii) a careful reordering of the parts and machines; and (iii) the establishment of upper bounds using the minimum possible contributions to the number of exceptional elements and voids for yet unassigned parts and machines. The scalability of the algorithm is limited by the number of parts and machines, as well as the inherent structure of the part–machine incidence matrix. Nevertheless, the proposed method produced globally optimal solutions for 104 test problems spanning 31 matrices from the literature, many of which are of nontrivial size. The new algorithm also compares favorably to a mixed-integer linear programming approach to the problem using CPLEX.

Effective two-phase p-median approach for the balanced cell formation in the design of cellular manufacturing system

In this paper, we present an effective two-phase p-median approach for the balanced cell formation (CF) in the design of cellular manufacturing system. In phase 1, the p-median mathematical model of machine CF, which adopts a linear integer programming formulation, is developed. Our formulation uses a new similarity coefficient based on the generalised nonbinary part-machine incidence matrix (PMIM) which incorporates realistic manufacturing aspects such as setup time, processing time, operation sequences and lot size of parts and duplicate machine types. In phase 2, a systematic part assignment procedure based on the new classification scheme of part types is established in pursuit of balancing the workload among machine cells. New efficiency measures for evaluating the quality of the binary and nonbinary PMIM-based block diagonal solutions are proposed to judge the degree of cell load imbalance. Computational experiments with moderately intermediate-sized data-sets selected from the literature show effectiveness of our two-phase p-median approach for the balanced CF.

A new linguistic based algorithm for creating manufacturing cell

This paper presents a new approach for modeling formation of machine groups and the associated parts families using a linguistic model. The method outlines a hierarchical approach for deciding the number of clusters/machine groups. The solution is improved further by utilizing a genetic algorithm with the objective of decreasing actual job-shop inter-cell distance moved. A unique feature of the proposed method is that it recognizes the operation sequence of manufacturing the parts specified in the process sheets. The concept of null machine is introduced in the linguistic model to calculate the dissimilarity among parts. The movement of parts can be either flow type or random job-shop type for the discrete manufacturing shop, required to be partitioned. Back flow/tracking are minimized. The flexibility of changing number of machine groups is also built-in the model. The model has been found useful and flexible enough to solve any realistic discrete manufacturing system and is found to work comfortably in medium to large job shop situations when the machine part incidence matrix shows sparse density.

Pareto-optimal front of cell formation problem in group technology

The earliest approaches to the cell formation problem in group technology, dealing with a binary machine-part incidence matrix, were aimed only at minimizing the number of intercell moves (exceptional elements in the block-diagonalized matrix). Later on this goal was extended to simultaneous minimization of the numbers of exceptions and voids, and minimization of intercell moves and within-cell load variation, respectively. In this paper we design the first exact branch-and-bound algorithm to create a Pareto-optimal front for the bi-criterion cell formation problem.

Cellular manufacturing system design using grouping efficacy-based genetic algorithm

This paper presents an algorithm for the design of manufacturing cells and part families. This algorithm is suitable for arriving at a good block diagonal structure for a cellular manufacturing design problem with part machine incidence matrix as input. The objective of this algorithm is the maximisation of grouping efficacy (GE), which is one of the most widely used measures of quality for cellular configurations. Assignment of machines to cells is using genetic algorithm, and part assignment heuristic is based on an effective customised rule. A comparison of the proposed algorithm is made with seven other methods of cell formation by taking 36 problems from the literature and found that the proposed algorithm is performing much better than the others. Finally, the algorithm is extended to form configurations with good GE when there are alternative routes.

A discrete particle swarm optimisation approach for manufacturing cell formation

Manufacturers need to improve the efficiency and productivity of their production activities to meet fluctuating demand for customised products with shorter life-cycles. Cellular manufacturing is one of the important manufacturing philosophies to achieve manufacturing flexibility. In the past, many methods have been developed for cell formation problem using zero-one machine part incidence matrix but such an approach can hardly incorporate real life production factors resulting in inefficient cell design. In the proposed approach, similarity coefficient is determined using various real life production factors. A discrete particle swarm optimisation approach is adopted to form the cluster of machines through maximising similarity coefficient within the cell and minimising similarity coefficient between the different cells. The proposed approach is tested on benchmark problems of different size from open literature for its performance using group technology efficiency and system utilisation.

GT efficacy: a performance measure for cell formation with sequence data

Cell formation in cell manufacturing design is a crucial step for improving productivity. As input data for the cell formation problem, a part-machine incidence matrix is given in binary format or in ordinal format. The solutions for effective cell formation have to be compared using performance measures. Grouping efficacy is used as a standard measure for evaluating solutions based on a binary part-machine matrix, which does not consider ordinal data. However, the representative measures, called standard measures, for the ordinal part-machine matrix are absent. The existing measures designed for ordinal data produce conflicting results and can lead to subjective decisions. In this paper, a new objective measure called group technology (GT) efficacy is proposed for ordinal data, reflecting on both intercellular movement and compactness within cells simultaneously. The advantage of GT efficacy is demonstrated by comparing it with a few previously-proposed measures.

Genetic algorithm approach for integrating cell formation with machine layout and cell layout

Cellular manufacturing system (CMS) is based on the principle of similar things should be done similarly. Cell formation (CF), within cell machine layout design and cell layout design are important steps in design of CMS. The existing models for solving CMS problems are focused mainly on CF. The design of machine layout and cell layout are considered in few research papers. The most of existing research papers have used binary data for CFs. They do not focus on production volume, operational sequences, production cost, inventory and other production data. In this research work, hierarchical genetic algorithm (HGA) approach is used to solve the CF, within cell machine layout design and cell layout design. The input data for this design of CMS is machine-part incidence matrix with operational sequence. The grouping efficiency and the grouping efficacy are used to measure the effectiveness of the CMS design. The results and consistency of the HGA are compared with earlier approaches and the results are presented. The proposed algorithm gives best results when compared to the earlier approaches.