Part Family Formation Research Articles

Execution of revised BMIM similarity coefficient for part family formation in reconfigurable manufacturing system

The reconfigurable manufacturing system (RMS) is an advanced manufacturing strategy that enables precise adjustment of functionality and capacity to meet fluctuating demands economically. RMS focuses on part families, allowing configurations to be adapted for new part requirements. Optimizing flow line design to produce various parts involves minimizing reconfigurations and associated costs by enhancing operation sequence similarity. This article proposes a novel sequence optimization using the Longest Common Subsequence (LCS) method to reduce bypassing moves and machine idle times. The study introduces a similarity coefficient derived from LCS and employs average linkage hierarchical clustering to categorize parts in a case study. Unlike traditional methods, this approach considers material movements both before the initial machine and after the final processing station, addressing gaps in bypassing move calculations. The impact of different weighting scenarios for Type-II moves (ω) and machine idleness (β) on clustering was examined. For example, with Type-II move weights (ω) of {1.0, 0.6, 0.3, 0.0} and equal weightings for bypassing moves (α) and machine idleness (β) set at 0.5, a threshold value of 0.3 results in eight clusters, such as Cluster 1 {1, 11, 10, 12} and Cluster 3 {3, 5, 6, 4, 15, 9, 13, 14, 7, 8}. Lower threshold values lead to fewer clusters with larger sizes, indicating a more consolidated part family grouping. Various Type-II move weights and material handling scenarios demonstrate how different weighting configurations affect clustering and part family sizes. This approach enhances RMS efficiency by integrating comprehensive material handling considerations and optimizing clustering based on operational similarities.

A methodology for part family formation in reconfigurable manufacturing systems using Shannon's entropy method

For effective utilization of resources, Operation Management plays an important role in businesses and organizations. It basically coordinates the efforts of people to fulfil the goals and objectives decided by the organization. Operation Management is a supporting system to carry out businesses. From the point of generation of raw material to the point of consumption of goods, it directs and controls the organization. It makes a path to a manufacturing system to fulfil its goal. For full utilization of resources in an uncertain environment, the manufacturing system faces various challenges in this century. The manufacturing systems must be responding rapidly to market changes. For effective management, a new paradigm in the manufacturing system is the Reconfigurable manufacturing systems, which are built around a part family to face such challenges in an uncertain environment. The authors have developed a methodology for part family formation using attributes modularity, reconfiguration effort, longest common subsequence, etc. in the present work. The developed methodology has been discussed using an example.

A multi-objective joint optimisation method for simultaneous part family formation and configuration design in delayed reconfigurable manufacturing system (D-RMS)

ABSTRACT In the era of Industry 4.0, the demand fluctuation has become fiercer due to the characteristics of diversification, customisation, and uncertainty. Reconfigurability of manufacturing systems has been proven to be a useful and necessary feature when it comes to handling demand uncertainty. This feature can be achieved through the implementation of reconfigurable manufacturing system (RMS) and delayed reconfigurable manufacturing system (D-RMS). D-RMS is a subclass of RMS that focuses primarily on improving the convertibility of the manufacturing system. The two main phases involved in implementing D-RMS are part family formation and configuration design. Therefore, we proposed a multi-objective joint optimisation method of part family formation and configuration design according to the philosophy of D-RMS. Firstly, we develop a multi-objective joint optimisation model that takes into account investment cost, reconfiguration cost, similarity coefficient, and delayed reconfiguration to optimise the part family and configuration of D-RMS simultaneously. Three types of machine tools namely dedicated machine tools, flexible machine tools, and reconfigurable machine tools are considered in the optimisation model. Secondly, the non-dominated sorting genetic algorithm-III (NSGA-III) is adopted to solve the proposed multi-objective integer programming problem. Finally, numerical experiments are presented to demonstrate the effectiveness of the proposed multi-objective joint optimisation method.

Proposed composite similarity metric method for part family formation in reconfigurable manufacturing system

Proposed composite similarity metric method for part family formation in reconfigurable manufacturing system

Part family formation method for delayed reconfigurable manufacturing system based on machine learning

Part family formation method for delayed reconfigurable manufacturing system based on machine learning

A two-phase part family formation model to optimize resource planning: a case study in the electronics industry

A two-phase part family formation model to optimize resource planning: a case study in the electronics industry

HUMAN COGNITION INSPIRED PROCEDURES FOR PART FAMILY FORMATION BASED ON NOVEL INSPECTION BASED CLUSTERING APPROACH

Human cognition based procedures are promising approaches for solving different kind of problems, and this paper addresses the part family formation problem inspired by a human cognition procedure through a graph-based approach, drawing on pattern recognition. There are many algorithms which consider nature inspired models for solving a broad range of problem types. However, there is a noticeable existence of a gap in implementing models based on human cognition, which are generally characterized by “visual thinking”, rather than complex mathematical models. Hence, the natural power of reasoning - by detecting the patterns that mimic the natural human cognition - is used in this study as this paper is based on the partial implementation of graph theory in modelling and solving issues related to part machine grouping, regardless of their size. The obtained results have shown that most of the problems solved by using the proposed approach have provided interesting benchmark results when compared with previous results given by GRASP (Greedy Randomized Adaptive Search Procedure) heuristics. Keywords: Cellular manufacturing systems; part family formation; human cognition; inspection-based clustering.

Assessment of a reconfigurable manufacturing system

PurposeThe purpose of this paper is to develop the methodology which can facilitate the concept of reconfiguration in the manufacturing system.Design/methodology/approachDesign methodology includes the calculation of similarity matrix, formation of part family, and selection of part family. ALC algorithm has been used for part family formation and three criteria have been considered for the selection of part family. These criteria are reconfiguration effort, under-utilization cost, and floor space cost. AHP has been used to calculate the weights of criteria and reference ideal method has been used for the selection of alternatives.FindingsIn the manufacturing system, machines should be grouped on the basis of reconfiguration cost. When the time period is less, light machines and Group 1 machines are added and removed. In the case study, the concept of reconfiguration is useful for families (A, B, C, D). Machines can be reused by adding/removing some modules of machines. The concept of reconfiguration becomes more useful when it is implemented with lean manufacturing. Lean manufacturing techniques Jidoka and Poka-yoke are used to increase the diagnosability of the system.Practical implicationsIndustrial case study has been considered.Social implicationsMarket competition is increasing rapidly and it increases the demand and variety of products, due to which manufacturing enterprises are forced to adapt a manufacturing system which can adjust its capacity and functionality quickly at low cost. To reconfigure manufacturing system from one product/product family to another product/product family, changes can be done in hardware and/or software components in response to sudden changes in the market or in regulatory requirements.Originality/valueAn integrated approach for reconfiguration has been proposed considering the industrial application. It includes weighted Jaccard function, ALCA, AHP, RIM. The methodology for calculation of reconfiguration effort, under-utilization cost, and floor space cost has been presented for industrial case.

Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system

Under a mass individualisation paradigm, the individualised design of manufacturing systems is difficult as it involves adaptive integrating both new and legacy machines for the formation of part families with uncertainty. A systematic virtual model mirroring the real world of manufacturing system is essential to bridge the gap between its design and operation. This paper presents a digital twin-driven methodology for rapid individualised designing of the automated flow-shop manufacturing system. The digital twin merges physics-based system modelling and distributed semi-physical simulation to provide engineering solution analysis capabilities and generates an authoritative digital design of the system at pre-production phase. An effective feedbacking of collected decision-support information from the intelligent multi-objective optimisation of the dynamic execution is presented to boost the applicability of the digital twin vision in the designing of AFMS. Finally, a bi-level iterative coordination mechanism is proposed to achieve optimal design performance for required functions of AFMS. A case study is conducted to prove the feasibility and effectiveness of the proposed methodology.

Framework for Part Family Formation Based on Setup Similarity

Framework for Part Family Formation Based on Setup Similarity

A MULTI-PERIOD GROUP TECHNOLOGY METHOD FOR DYNAMIC CELLULAR MANUFACTURING SYSTEMS

A dynamic cellular manufacturing system is a system where cell configurations change in order to respond to changing production requirements. This paper proposes methods for multi-period cell and part family formation. The method of fuzzy C-means clustering is used for part family formation while balancing the part load between cells in each period; a genetic algorithm is presented to solve the model. An integer programming model is presented for multi-period cell formation with the objective of determining an optimal trade-off between reconfiguration cost and the cost of deficit capacity.

Formation of part family for reconfigurable manufacturing systems considering bypassing moves and idle machines

To address the problem of how to allow a reconfigurable manufacturing system to simultaneously consider efficiency and flexibility, a method is presented for the formation of part family that considers bypassing moves and idle machines. First, the longest common subsequence (LCS) among different part processes is identified. Then, based on this LCS, the shortest composite supersequence (SCS) is constructed by combining the LCS and the rest of the operation while giving consideration to bypassing moves and idle machines. Based on the linear relationship of the similarity of parts for the LCS and SCS, a similarity coefficient algorithm is designed, which is used as the basis for part clustering and family formation. Finally, the developed similarity coefficient algorithm is compared with existing similarity coefficient algorithms to verify its efficiency and accuracy.

Dynamic virtual cellular manufacturing through agent-based modelling

In this paper, a new virtual cellular manufacturing approach is developed considering dynamic demand arrivals. In order to obtain an efficient integrated method, agent-based modelling approach is used. Since agent-based modelling has the ability to track and evaluate real time information successfully and to model the complex systems effectively. The proposed approach enables to realise part family formation, virtual cell formation and scheduling phases simultaneously. The applicability and efficiency of the proposed approach is tested via several scenarios which are prepared considering various parameters and strategies of manufacturing. The results are discussed and it is shown that the proposed approach is promising in dynamic manufacturing environments.

A neural network-based approach for part family classification for a reconfigurable manufacturing system

The design of RMS initiates with the classification of parts into families, after which reconfiguration of the system is carried out to cater new part families. It is important that parts must be grouped into logical families based on similarities either in manufacturing or design attributes. Generally, production system maintains a large database of existing part families, and once any new part comes in, the efforts must be focused on deciding upon an appropriate existing part family in which the new part may be grouped with. In literature, most of the approaches are based on part family formation from beginning with no consideration of how the existing part family database can be utilised to decide upon a suitable existing part family for a new part. This paper proposed a neural network classification-based approach for such classification. The developed methodology is explained with the help of a numerical illustration.

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.

Part family formation using skip moves and lazy machines in a reconfigurable manufacturing system

With the advent of modern manufacturing systems like RMSs many issues pertaining to its design and implementation has been taken up in the recent past. As per definition, RMS design initiates with the classification of product into part families. Subsequent reconfiguration of the system is carried out to cater new part families. The issue of part family formation in RMS is considered to be of utmost importance for this new manufacturing paradigm, i.e., RMS as the complete philosophy of its design and operation revolves around part families. Thus, it becomes exceeding important that parts must be grouped into logical families based on similarities in manufacturing or design features to physically realise the implementation of RMSs. Though some techniques for such formation for RMSs were proposed in the past but the present technique eliminates some of the shortcoming in those proposed techniques. In the present paper, an operations string-based methodology known as skip moves and lazy machines (SMLM) has been undertaken for part family formation. The paper also discusses on some of the early similarity indices developed using operation sequencing. The technique is demonstrated using a numerical example and the results are finally discussed in light of some previous researches on the topic.

Part Family Formation Based on Clustering Algorithm and BP Network

To solve the problem of design and manufacturing on the production of many varieties of small batch, the parts grouping method that based on clustering algorithm, clustering validity index and BP neural network method for new parts is proposed. At first, mathematical model of part clustering is built, and parts grouping is based on the similarity of data and cluster centers which is calculated by Euclidean distance, then the effectiveness of parts group is tested by Function Index and optimal number of clusters group can be found. The algorithm is achieved by Matlab clustering toolbox, so the best part family structure is built. Furthermore, the grouped parts are used to train the BP neural network toolbox in Matlab, then simulate new parts on network to find the match group. At last, a case study was also presented to verify the feasibility of this method．

Agent-based dynamic part family formation for cellular manufacturing applications

One of the main critique on cellular manufacturing and its algorithms is their inability to handle dynamics events, especially dynamic changes in part spectrum. Unfortunately, there are not many efforts in the literature to overcome this problem. Agent oriented computing provides a marvellous opportunity to handle dynamic problems and to provide effective solutions, if carefully and intelligently implemented. In this paper, we have proposed a novel agent-based clustering algorithm for part family formation in cellular manufacturing by considering dynamic demand changes. However, it is not easy to directly compare the performance of the proposed algorithm with the literature results as there is no benchmark for dynamic cell formation problems. We attempt to compare the performance of the present algorithm on static test problems by dynamically introducing parts in these data-sets to our algorithm. Many results have been presented on these static data-sets by utilising several heuristics, meta-heuristics and optimisation-based algorithms. Although the proposed algorithm is not an optimisation-based algorithm and its operation is directed to handle dynamic changes in the problem domain through negotiation, we have shown that it has ability to provide very good results which are comparable to the best known solutions.

Machine-cell and Part-family Formation in Cellular Manufacturing Using a Two-phase Clustering Algorithm

This paper presents a two-phase clustering algorithm for machine-cell and part-family formation in the design of cellular manufacturing systems. The proposed algorithm begins with the determination of initial cluster centers via a linear assignment method using the least similar group representatives in its first phase. A fuzzy C-means clustering method is followed in its second phase for part-family and machine-cell formation using the obtained initial cluster centers. The two-phase algorithm can remedy the problem of clustering inconsistency resulting from the fuzzy C-means method with random initializations. Experimental results on many benchmark data sets based on multiple performance criteria substantiate the effectiveness of the proposed algorithm.

Part family formation for reconfigurable manufacturing system using K-means algorithm

The reconfigurable manufacturing systems (RMSs) is the next step in manufacturing, allowing the production of any quantity of highly customised and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system is reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various reconfigurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of reconfigurable manufacturing systems (RMSs). The methodology carried out in two phases. In the first phase, the correlation matrix is used as similarity coefficient matrix to form the part operational similarity followed by agglomerative hierarchical K-means algorithm used for the parts family formation. In the second phase, cost model is developed to select and sequence the formed part families, resulting in a minimum cost solution to the problem.