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Fractional cell formation - Issues and approaches
Abstract
This paper addresses Fractional cell formation in Group Technology implementation, where part of the production facility is redesigned into manufacturing cells. The fractional cell formation issue is important in manufacturing cell design, because it may not be possible to economically convert the entire facility to manufacturing cells. The authors discuss the issues in modeling fractional cell formation problems and they develop efficient algorithms to group the facilities into manufacturing cells and a remainder cell. Algorithms are tested using a variety of matrices including those from real-life situations to demonstrate the potential applicability of the proposed approach.
... The objective of GT layout is to streamline material flow in job shop production by grouping the production facilities into cells and assigning jobs to each cell. GT layouts help to achieve the goal of JIT and TQM (Co and Araar, 1988, Onwubolu and Mlilo, 1998, Srinivasan and Zimmers, 1998. It is not possible to convert the entire plant into cells. ...
Unlike in a focussed system, which offers many benefits for the organisation, the conventional factory attempts to do too many conflicting production tasks within one inconsistent set of manufacturing resources. In the past, the issue of converting a functionally focused manufacturing system into cellular manufacturing had not been adequately addressed. The present work is an attempt in this direction. Specifically, it addresses the issue of gearing up product focussed cells. A mixed integer programming formulation has been proposed with an objective of maximising product ownership at cell level. The solution methodology involves a three stage heuristic approach. Beginning with decomposition of the problem at stage one, the heuristic progressively constructs product-focused cells. The proposed heuristic has been applied to an organisation engaged in the manufacture of equipment for the electrical industry. The result indicates that the model has the potential to be an effective tool for decomposition of functionally organised system into product focused cells with interval data.
... They used simulated annealing (SA) and heuristics algorithms (HA) for fractional cell formation. In other research, Srinivasan and Zimmers [2] used a neighborhood search algorithm for fractional cell formation. ...
Group technology (GT) is a manufacturing philosophy that attempts to reduce production cost by reducing the material handling
and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known
as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize
the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1
network (ART1). The input to the modified ART1 is machine-part incidence matrix comprising of the binary digits 0 and 1. This
method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other
algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect
to other known algorithms/heuristics in terms of computational speed and consistency are presented.
... Gupta (1993) proposed a similarity coefficient which incorporates several production factors such as operation sequences, production volumes, alternative process routings. Lee and Garcia-Diaz (1996) Hamming (D) Use a 3-phase network-flow approach Leem and Chen (1996) Jaccard Fuzzy set theory Lin et al. (1996) Bray-Curtis (D) Heuristic; workload balance within cells Sarker (1996) Many Review of similarity coefficient Al-sultan and Fedjki (1997) Hamming (D) Genetic algorithm Askin et al. (1997) MaxSC Consider flexibility of routing and demand Baker and Maropoulos (1997) Jaccard, Baker and Maropoulos (1997) Black box clustering algorithm Cedeno and Suer (1997) -Approach to ''remainder clusters'' Masnata and Settineri (1997) Euclidean (D) Fuzzy clustering theory Mosier et al. (1997) Many Review of similarity coefficients Offodile and Grznar (1997) Offodile (1991) Parts coding and classification analysis Wang and Roze (1997) Jaccard, Kusiak (1987), CS Modify p-median model Cheng et al. (1998) Manhattan (D) TSP by genetic algorithm Jeon et al. (1998a) Jeon et al. (1998b p-median Onwubolu and Mlilo (1998) Jaccard A new algorithm (SCDM) Srinivasan and Zimmers (1998) Manhattan (D) Fractional cell formation problem Wang (1998) -A linear assignment model Ben-Arieh and Sreenivasan (1999) Euclidean (D) A distributed dynamic algorithm Lozano et al. (1999) Jaccard Tabu search Sarker and Islam (1999) Many Performance study Baykasoglu and Gindy (2000) Jaccard Tabu search Chang and Lee (2000) Kusiak (1987) Multi-solution heuristic Josien and Liao (2000) Euclidean (D) Fuzzy set theory Lee-post (2000) Offodile (1991) Use a simple genetic algorithm Won (2000a) Won and Kim(1997) Alternative process plan with p-median model Won (2000b) Jaccard, Kusiak (1987) Two-phase p-median model Dimopoulos and Mort (2001) Jaccard Genetic algorithm Samatova et al. (2001) Five dissimilarity coefficients Vector perturbation approach a No specific SC mentioned. Akturk and Balkose (1996) revised the Levenshtein distance measure to penalize the backtracking parts neither does award the commonality. ...
The initial step in the design of a cellular manufacturing (CM) system is the identification of part families and machine groups and forming manufacturing cells so as to process each part family within a machine group with minimum inter-cellular movements of parts. One methodology to form manufacturing cells is the use of similarity coefficients in conjunction with clustering procedures. In this paper, we give a comprehensive overview and discussion for similarity coefficients developed to date for use in solving the cell formation (CF) problem. More than 160 sources from premier scientific journals, conferences and books have been reviewed. Despite previous studies indicated that similarity coefficients based method (SCM) is more flexible than other CF methods, none of the studies has explained the reason why SCM is more flexible. This paper tries to explain the reason explicitly. We also develop a taxonomy to clarify the definition and usage of various similarity coefficients in designing CM systems. Existing similarity (dissimilarity) coefficients developed so far are mapped onto the taxonomy. Additionally, production information based similarity coefficients are discussed and a historical evolution of these similarity coefficients is outlined. Finally, recommendations for future research are suggested in this paper.
Changes in demand, as one of the issues of volatile manufacturing systems, decline the performance of manufacturing systems over the time; especially, it degrades the effectiveness of layout in manufacturing systems. Although the layout of facilities which is the arrangement of facilities in the shop floor plays a significant role in the effectiveness of manufacturing systems, it has not absorbed the attention of researchers in hybrid manufacturing systems. In this paper, a new mathematical model for facility layout in a hybrid cellular manufacturing system has been proposed that considers demand varying over the planning horizon. The model minimizes the material handling cost. To solve the model, a simulated annealing algorithm from literature has been improved. The comparison of results between two algorithms shows the superiority of the improved algorithm in both the quality of solutions and computational time.
The primary motivation for adopting cellular manufacturing is the globalization and intense competition in the current marketplace.
The initial step in the design of a cellular manufacturing system is the identification of part families and machine groups
and forming manufacturing cells so as to process each part family within a machine group with minimum intercellular movements
of parts. One methodology to manufacturing cells is the use of similarity coefficients in conjunction with clustering procedures.
In this chapter, we give a comprehensive overview and discussion for similarity coefficients developed to date for use in
solving the cell formation problem. Despite previous studies indicated that the similarity coefficients-based method (SCM)
is more flexible than other cell formation methods, none of the studies has explained the reason why SCM is more flexible.
This chapter tries to explain the reason explicitly. To summarize various similarity coefficients, we develop a taxonomy to
clarify the definition and usage of various similarity coefficients in designing cellular manufacturing systems. Existing
similarity (dissimilarity) coefficients developed so far are mapped onto the taxonomy. Additionally, production information
based similarity coefficients are discussed and a historical evolution of these similarity coefficients is outlined. Although
many similarity coefficients have been proposed, very fewer comparative studies have been done to evaluate the performance
of various similarity coefficients. In this chapter, we compare the performance of twenty well-known similarity coefficients.
More than two hundred numerical cell formation problems, which are selected fromthe literature or generated deliberately,
are used for the comparative study. Nine performance measures are used for evaluating the goodness of cell formation solutions.
Two characteristics, discriminability and stability of the similarity coefficients are tested under different data conditions.
From the results, three similarity coefficients are found to be more discriminable; Jaccard is found to be the most stable
similarity coefficient. Four similarity coefficients are not recommendable due to their poor performances.
The primary objective of group technology (GT) is to enhance the productivity in batch manufacturing environment. The GT cell formation and fractional cell formation are done by using Kohonen self-organizing map (KSOM) networks. The effectiveness of the cell formation is measured with number of exceptional elements, bottleneck parts and grouping efficiency and the effectiveness of the fractional cell formation is measured by number of exceptional elements and the number of machines in the reminder cell. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or best when compared to the other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.
Cell design in Cellular Manufacturing Systems (CMS) assumes significance due to the incremental nature of implementation. In this study a hybrid heuristic has been developed considering incremental aspect of implementation. The heuristic identifies complete cells (if they exist), otherwise forms a CMS with combination of regular cells and a remainder cell which caters to the needs of exceptional parts/operations. The heuristic has been tested with the standard problems in the literature. It is found that the proposed heuristic produces superior solutions in 62.22% cases and the same solutions in the remaining cases. Further, statistical testing has been conducted and found that the performance of the proposed heuristic is significantly better than the existing methods.
This paper presents a discussion of the variables affecting the control of a group technology, cellular manufacturing system. The control function is partitioned into cell loading and cell scheduling. The variables affecting the control of a cellular manufacturing system include the characteristics that describe the physical cellular system and the characteristics that describe the jobs. In addition, the paper reviews many of the advantages and disadvantages of cellular manufacturing, and the implied assumptions. Finally, a brief discussion of the problems associated with the techniques for the successful loading and scheduling of the cellular manufacturing system is presented.
Group Technology (GT) aims at improving productivity in batch manufacturing. Here components are divided into families and machines into cells such that every component in a part family visits maximum number of machines in the assigned cell with an objective of minimizing inter-cell movement. In situations where too many inter-cell moves exist, fractional cell formation using remainder cells can be used. Here, machines are grouped into GT cells and a remainder cell, which functions like a job shop. Component families are formed such that the components visit the assigned cell and the remainder cell and do not visit other cells. The fractional cell formation problem to minimise inter-cell moves is formulated as a linear integer programming problem. Here, movement between machine cells and remainder cells is not counted as inter-cell moves but movement of components among GT cells is considered as inter-cell movement. The fractional cell formation problem is solved using Simulated Annealing. A heuristic algorithm is developed to solve large sized GT matrices. These have applied to a variety of matrices from GT literature and tested on randomly generated matrices. Computational experiences with the algorithms are presented
An efficient nonhierarchical clustering algorithm, based on initial seeds obtained from the assignment method, for finding part-families and machine cells for group technology (GT) is presented. By a process of alternate clustering and generating seeds from rows and columns, the zero-one machine-component incidence matrix was block-diagonalized with the aim of minimizing exceptional elements (intercell movements) and blanks (machine idling). The algorithm is compared with the existing nonhierarchical clustering method and is found to yield favourable results.
This paper deals with the development of an algorithm for concurrent formation of part-families and machine-cells in group technology. The acronym ZODTAC stands for zero-one data: ideal seed algorithm for clustering. The present algorithm is an expanded and improved version of the earlier ideal seed method. The formation of part-families and machine-cells has been treated as a problem of block diagonalization of the zero-one matrix. Different methods of choosing seeds have been developed and tested. A new concept called ‘relative efficiency’ has been developed and used as a stopping rule for the iterations. The ZODIAC procedure and its theory are given in detail. Test results with a 40 × 100 matrix are shown.
The machine-part group formation is an important issue in the design of cellular manufacturing systems. The present paper first discusses some of the alternative formulations of this problem, their advantages and disadvantages, and then suggests a new linear zero-one formulation which seems to have removed most of the disadvantages observed in other models. It will be shown that most of the integrality conditions of the proposed formulation can be relaxed. This considerably improves its computational feasibility and efficiency. Finally, a simulated annealing approach to deal with large-scale problems is also presented.