Cell design and continuous improvement

Baker, R. P.; Maropoulos, Paul

doi:10.1080/09511920050195959

Cited by 6 publications

(4 citation statements)

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“…They reported that SLINK causes more rugged chaining problem than other investigated methods. In addition, comparing to CLINK, SLINK mostly provided smaller cells Further, Baker & Maropoulos (2000) proposed a 3-stage method for CF, layout design and capability analysis to configure, generate and find approximated positions of cells and workstations within them. Irani and Huang (2000) presented a mathematical programming (MP) model for minimizing sum of inter-modular and machine duplications in large scale problems.…”

Section: Hierarchical Clustering Methodsmentioning

confidence: 99%

“…Proposing a modified version of ART1/modified procedure that stores group representation vectors 55 Baker & Maropoulos (2000) Using BBC/Finding optimal flow of parts/Using capability target for improving solutions Proposing a 3 stage framework for configure, cluster and find approximated position of the cells and workstations within them 56 Irani & Huang (2000) Using similarity coefficients/ minimizing sum of intermodule travels and machine duplication Reordering input vectors to Enke et al (2000) Applying for a modified procedure that stores group representation vectors ART1 Presented a two phase modified ART based algorithm Pandian & Mahapatra (2009) Considering operation sequence and operation time of the parts Fuzzy ART Using fuzzy MAX operator instead of fuzzy MIN operator Ozdemirel et al (1993) Solving proliferation problem Fuzzy ART Developed Fuzzy ART for CFP Burke & Kamal (1995) Providing solutions with minimum inter-cell and cellular movements…”

Section: Comparison Of Clustering Methodsmentioning

confidence: 99%

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Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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This paper presents a review of clustering and mathematical programming methods and their impacts on cell forming (CF) and scheduling problems. In-depth analysis is carried out by reviewing 105 dominant research papers from 1972 to 2017 available in the literature. Advantages, limitations and drawbacks of 11 clustering methods in addition to 8 meta-heuristics are also discussed. The domains of studied methods include cell forming, material transferring, voids, exceptional elements, bottleneck machines and uncertain product demands. Since most of the studied models are NP-hard, in each section of this research, a deep research on heuristics and metaheuristics beside the exact methods are provided. Outcomes of this work could determine some existing gaps in the knowledge base and provide directives for objectives of this research as well as future research which would help in clarifying many related questions in cellular manufacturing systems (CMS).

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Section: Hierarchical Clustering Methodsmentioning

confidence: 99%

Section: Comparison Of Clustering Methodsmentioning

confidence: 99%

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Delgoshaei¹,

Delgoshaei²,

Ali³

2019

10.5267/j.ijiec

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“…The results show that the proposed DPA improves system performance by reducing the congestion in machines with long waiting lines. Baker and Maropoulos (2000) attempted to minimize material transferring movements by finding the best position of cells and workstations within them in order to find the optimal flow of parts. By contrast, Solimanpur et al (2004) addressed a QAP model to minimize intercellular WIP transferring in which cell locations were pre-defined.…”

Section: Fig 1 Intracellular and Intercellular Materials Transferringmentioning

confidence: 99%

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Delgoshaei¹,

Ariffin²,

Baharudin³

et al. 2016

10.5267/j.ijiec

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Cell load variation is considered a significant shortcoming in scheduling of cellular manufacturing systems. In this article, a new method is proposed for scheduling dynamic cellular manufacturing systems in the presence of bottleneck and parallel machines. The aim of this method is to control cell load variation during the process of determining the best trading off values between in-house manufacturing and outsourcing. A genetic algorithm (GA) is developed because of the high potential of trapping in the local optima, and results are compared with the results of LINGO ® 12.0 software. The Taguchi method (an L_9 orthogonal optimization) is used to estimate parameters of GA in order to solve experiments derived from literature. An in-depth analysis is conducted on the results in consideration of various factors, and control charts are used on machine-load variation. Our findings indicate that the dynamic condition of product demands affects the routing of product parts and may induce machine-load variations that yield to cell-load diversity. An increase in product uncertainty level causes the loading level of each cell to vary, which in turn results in the development of "complex dummy sub-cells". The effect of the complex sub-cells is measured using another mathematical index. The results showed that the proposed GA can provide solutions with limited cell-load variations.

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“…Based on their experience, Baker and Maropoulos (2000) propose to re-organize only a small part of the shop into cells instead of a complete transformation. In other words, their approach creates a hybrid layout where CM and FL cells could exist concurrently.…”

Section: Configuring a Hybrid Workhopmentioning

confidence: 99%

Hybrid organization of functional departments and manufacturing cells in the presence of imprecise data

Feyzıog̃lu

Pierreval

2008

International Journal of Production Research

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Although the benefits of forming cells are widely recognized, certain industrial decision makers do not wish an entire transformation because of possible changes that may occur, for example, in the demand, mix or routing of products. Therefore, hybrid organization of functional departments and manufacturing cells in the same shop may appear quite relevant. The problem addressed here is how to allocate machines and products so as to design such systems that take advantages of both types of layout. We suggest an approach that favours cells for the parts that are regularly manufactured over time (i.e. stable parts), and functional departments for the parts that require more flexibility. We also consider the difficulties arising in the collection of precise data in reality. Hybrid shop design problem is formulated as a constrained fuzzy multi-objective optimization problem and an evolutionary programming algorithm is devised to solve it. The approach is illustrated in test examples.

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Cell design and continuous improvement

Cited by 6 publications

References 0 publications

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

Evolution of clustering techniques in designing cellular manufacturing systems: A state-of-art review

A new method for decreasing cell-load variation in dynamic cellular manufacturing systems

Hybrid organization of functional departments and manufacturing cells in the presence of imprecise data

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