Evaluation of leanness using fuzzy association rules mining

Vinodh, S.; Prakash, N.; Selvan, K. Eazhil

doi:10.1007/s00170-011-3260-3

Cited by 32 publications

(15 citation statements)

References 16 publications

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“…In this paper, we substitute the sigmoid function by a more proper one in (11) to overcome the disadvantage of randomly changing position when the particle's velocity goes to zero.…”

Section: Binary Particle Swarm Optimizationmentioning

confidence: 99%

“…The local best and global best values are recorded and updated by iteration. The velocities of the particles are updated by (11). After updating the velocities, the new positions of the particles are updated according to the following strategy: a big value for V shows the position is not good and changes from 0 to 1 or vice versa.…”

Section: Application Procedures Of Bpso-armmentioning

confidence: 99%

“…Data mining techniques, especially association rule mining (ARM), has been applied successfully in industrial cases [3][4][5][6][7][8][9][10][11][12][13][14]. The discovered association rules are utilized in three major fields: quality control, product design, and manufacturing process control.…”

Section: Introductionmentioning

confidence: 99%

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Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Kou

2018

Mathematical Problems in Engineering

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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

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“…In this paper, we substitute the sigmoid function by a more proper one in (11) to overcome the disadvantage of randomly changing position when the particle's velocity goes to zero.…”

Section: Binary Particle Swarm Optimizationmentioning

confidence: 99%

Section: Application Procedures Of Bpso-armmentioning

confidence: 99%

See 1 more Smart Citation

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Kou

2018

Mathematical Problems in Engineering

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“…Vimal and Vinodh [24] proposed a leanness evaluation technique using a set of IF-THEN rules based on fuzzy logic and targeting five enablers: management responsibility, manufacturing management, workforce leanness, technology leanness and manufacturing strategy. Vinodh and Prakash [25], proposed the application of Fuzzy Association Rules Mining (FARM) to analyse the level of leanness of a manufacturing company in India. Seven attributes were considered to reflect the performance from a perspective of leanness: cost, profitability, productivity, quality, lead time, defects and availability.…”

Section: Lean Tools Selectionmentioning

confidence: 99%

Lean Maintenance Applied to Improve Maintenance Efficiency in Thermoelectric Power Plants

2017

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Thermoelectric power plants consist of a set of critical equipment that require high levels of availability and reliability. Due to this, maintenance of these physical assets is gaining momentum in industry. Maintenance is considered as an activity that contributes to improving the availability, efficiency and productivity of each piece of equipment. Several techniques have been used to achieve greater efficiencies in maintenance, among which we can find the lean maintenance philosophy. Despite the wide diffusion of lean maintenance, there is no structured method that supports the prescription of lean tools applied to the maintenance function. This paper presents the experience gathered in two lean maintenance projects in thermoelectric power plants. The application of lean techniques was based on using a previously developed multicriterial decision making process that uses the Fuzzy Analytic Hierarchy Process (AHP) methodology to carry out a diagnosis and prescription tasks. That methodology allowed the prescription of the appropriated lean techniques to resolve the main deficiencies in maintenance function. The results of applying such lean tools show that important results can be obtained, making the maintenance function in thermoelectric power plants more efficient and lean.

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“…Knowledge discovery models and particularly association rules discovery techniques were used successfully in many industrial applications [13][14][15][16][17][18][19][20][21][22][23]. For instance, in quality control, Da Cunha et al [13] studied the association between the assembly sequence and the likelihood of having defective products and utilized it in sequencing of modules and forming product families which minimizes the cost of production faults.…”

Section: Introductionmentioning

confidence: 99%

Assembly system synthesis using association rule discovery

ElMaraghy

Kashkoush

2015

Int J Adv Manuf Technol

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Assembly is the final process for product realization in which various parts, modules, and sub-assemblies are put together to build a product. Designing an assembly system typically involves many issues such as assembly equipment selection, assembly sequence planning, system layout, task assignments, and line balancing. Assembly system synthesis, specifically the choice of assembly resources/equipment, is examined in this paper. An association rule discovery model is employed to extract association relationships between existing and/or previous products and the systems used to assemble them. The extracted knowledge is used to synthesize assembly systems for new products that fall within the scope of their predecessors. The developed method is demonstrated using two examples from the automotive industry. System synthesis results that are consistent with the used assembly data instances are obtained in both examples. The presented systematic system synthesis method supports the utilization of existing legacy data in developing assembly systems for new product generations. This will lead to a significant reduction of time and effort needed to design assembly systems, particularly in applications that feature frequent design changes and updates, as in the automotive industry.

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Evaluation of leanness using fuzzy association rules mining

Cited by 32 publications

References 16 publications

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

Lean Maintenance Applied to Improve Maintenance Efficiency in Thermoelectric Power Plants

Assembly system synthesis using association rule discovery

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