Cell formation in manufacturing systems through simulated annealing: An experimental evaluation

Venugopal, V.; Narendran, T.T.

doi:10.1016/0377-2217(92)90158-6

Cited by 77 publications

(43 citation statements)

References 14 publications

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“…It is evident from the survey that many researchers do not consider some shop-floor realities due to their use of binary data for cell formation. In addition, in the case of zero-one MCIM, issues such as the sequence of manufacturing operations for components, non-consecutive operations performed on the same machine, volume of inter-cell moves, machine capacity and processing time go unaddressed [18,24]. The process of clustering machines into cells (and jobs or components into part-families) without using such information may lead to inferior manufacturing plans [11,12].…”

Section: Introductionmentioning

confidence: 99%

An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data

George

Rajendran

Ghosh

2003

The International Journal of Advanced Manufacturing Technology

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In this paper, the problem of clustering machines into cells and components into part-families with the consideration of ratio-level and ordinal-level data is dealt with. The ratio-level data is characterized by the use of workload information obtained both from per-unitprocess times and production quantity of components, and from machine capacity. In the case of ordinal-level data, we consider the sequence of operations for every component. These data sets are used in place of conventional binary data for arriving at clusters of cells and part-families. We propose a new approach to cell formation by viewing machines, and subsequently components, as 'points' in multi-dimensional space, with their coordinates defined by the corresponding elements in a Machine-Component Incidence Matrix (MCIM). An iterative algorithm that improves upon the seed solution is developed. The seed solution is obtained by formulating the given clustering problem as a Traveling Salesman Problem (TSP). The solutions yielded by the proposed clustering algorithm are found to be good and comparable to those reported in the literature.

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Section: Introductionmentioning

confidence: 99%

An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data

George

Rajendran

Ghosh

2003

The International Journal of Advanced Manufacturing Technology

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“…The second example is obtained from Venugopal and Narendran [10], and it has 20 machines and 20 parts. The nonbinary value between and is interpreted as the normalized workload of required to produce , and it is calculated based on processing time, machine available time, and production volume.…”

Section: Three Examples To Examine the Basic Functionalitymentioning

confidence: 99%

“…For comparison, Figure 8(b) shows the original solution by Venugopal and Narendran [10] that was obtained by using simulated annealing. These two solutions are almost the same except that the original solution has only four cells.…”

Section: Three Examples To Examine the Basic Functionalitymentioning

confidence: 99%

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Zhu

2018

Mathematical Problems in Engineering

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The purpose of this paper is to advance the similarity coefficient method to solve cell formation (CF) problems in two aspects. Firstly, while numerous similarity coefficients have been proposed to incorporate different production factors in literature, a weighted sum formulation is applied to aggregate them into a nonbinary matrix to indicate the dependency strength among machines and parts. This practice allows flexible incorporation of multiple production factors in the resolution of CF problems. Secondly, a twomode similarity coefficient is applied to simultaneously form machine groups and part families based on the classical framework of hierarchical clustering. This practice not only eliminates the sequential process of grouping machines (or parts) first and then assigning parts (or machines), but also improves the quality of solutions. The proposed clustering method has been tested through twelve literature examples. The results demonstrate that the proposed method can at least yield solutions comparable to the solutions obtained by metaheuristics. It can yield better results in some instances, as well.

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“…In the present days, most of the cell formation problems have been solved by using meta-heuristic algorithms. Meta-heuristic search approaches, such as simulated annealing [12][13][14] and a genetic algorithm [15], have also been used to solve various cell formation problems. Prabhakaran [16] has discussed the cell load variation and intercellular moves in cell formation problem using genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

Arunagiri¹,

Uthayakumar

Gopalakrishnan³

et al. 2017

Sustainability

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Abstract:The efficiency and sustainability of a cellular manufacturing system (CMS) in batch type manufacturing is highly valued. This is done using a systematic method of equipment into machine cells, and components into part families, based on the suitable similar criteria. The present work discusses the cell formation problem, with the objective of minimizing the cumulative cell load variation and cumulative intercellular moves. The quantity of parts, operation sequences, processing time, capacity of machines, and workload of machineries were considered as parameters. For the grouping of equipment, the modified artificial bee colony (MABC) algorithm is considered. The computational procedure of this approach is explained by using up to 40 machines and 100 part types. The result obtained from MABC is compared with the findings acquired from the genetic algorithm (GA) and ant colony system (ACS) in the literature.

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Cell formation in manufacturing systems through simulated annealing: An experimental evaluation

Cited by 77 publications

References 14 publications

An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data

An analytical-iterative clustering algorithm for cell formation in cellular manufacturing systems with ordinal-level and ratio-level data

Simultaneous Hierarchical Clustering for Cell Formation Problems with Production Information

Sustainability Formation of Machine Cells in Group Technology Systems Using Modified Artificial Bee Colony Algorithm

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