A fuzzy linguistic approach to data quantification and construction of distance measures for the part family formation problem

Gill, Adrian; Bector, C. R.

doi:10.1080/002075497194660

Cited by 12 publications

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“…But this process will become highly impractical if the number of parts and number of attributes are high and, moreover, the part families are to be still formed based on this code. Gill and Bector (1997) suggested a fuzzy linguistics approach for quantification and construction of distance measures for part family formation. They assigned the linguistic weights to the part attributes based on the judgement and constructed a distance matrix for the parts.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy part family formation for cellular manufacturing systems

Sangwan

Kodali

2004

Production Planning & Control

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Most of the techniques related to part family formation require precise numerical data for part's features. However, in many situations, the parts features are too illdefined and are too susceptible to analysis by traditional discrete methods. Fuzzy set theory allows the parts to belong to different groups with different memberships. Moreover, different attributes may not have equal importance. The AHP (analytical hierarchy process) model gives the weightage for different attributes and also checks the consistency of the judgements. This paper suggests two models for part family formation using fuzzy mathematics and the AHP model. One model is based on fuzzy logic and AHP and the second model uses fuzzy equivalence ratios and AHP. Models can be integrated with the existing coding systems used by organizations. Both models are validated using problems from literature.

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy part family formation for cellular manufacturing systems

Sangwan

Kodali

2004

Production Planning & Control

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“…Gill and Bector (1997) used a fuzzy linguistic approach to quantify part feature information for the part family formation problem.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Intelligent systems in manufacturing: current developments and future prospects

Meziane

Vadera

Kobbacy

et al. 2000

Integrated Manufacturing Systems

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Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence (AI) will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of AI techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different AI techniques to be considered and then shows how these AI techniques are used for the components of IMS.

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“…A number of researchers dealt with the formation of parts families (Xu and Wang (1989), Gill and Bector (1997), Ben-Arien and Triantaphyllou (1992)), whereas others considered fuzzy data within traditional approaches (Chu and Hayya (1991), Zhang and Wang (1992), Ravichandran and Rao (2001)). Moreover, other authors (Tsai, Chu and Barta (1997)) considered the application of fuzzy models for measuring uncertainty via a new proposed operator minimising the cost of exceptional elements.…”

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confidence: 99%

Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling

Papaioannou

Wilson

2008

Ann Oper Res

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Cell formation has received much attention from academicians and practitioners because of its strategic importance to modern manufacturing practices. Existing research on cell formation problems using integer programming (IP) has achieved the target of solving problems that simultaneously optimise: (a) cell formation (b) machine-cell allocation, and (c) part-machine allocation.This paper will present extensions of the IP model where part-machine assignment and cell formation are addressed simultaneously, and also a significant number of constraints together with an enhanced objective function are considered. The main study examines the integration of inter-cell movements of parts and machine set-up costs within the objective function, and also the combination of machine set-up costs associated with parts revisiting a cell when part machine operation sequence is taken into account. The latter feature incorporates a key set of constraints which identify the number of times a part travels back to a cell for a later machine operation.Due to two main drawbacks of IP modelling for cell formation, i.e. (a) only one objective function can be involved and (b) the decision maker is required to specify precisely goals and constraints, fuzzy elements like fuzzy constraints and fuzzy goals will be considered in the proposed model.Overall the paper will not only include an extended and enhanced integer programming model for assessing the performance of cell formation, but also perform a rigorous study of fuzzy integer programming and demonstrate the feasibility of achieving better and faster clustering results using fuzzy theory.

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A fuzzy linguistic approach to data quantification and construction of distance measures for the part family formation problem

Cited by 12 publications

References 0 publications

Fuzzy part family formation for cellular manufacturing systems

Fuzzy part family formation for cellular manufacturing systems

Intelligent systems in manufacturing: current developments and future prospects

Fuzzy extensions to Integer Programming models of cell-formation problems in machine scheduling

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