Modelling of FMS control policy: AIS-based fuzzy expert system

Prakash, Anuj; Deshmukh, S.G.

doi:10.1504/ijise.2011.040765

Cited by 4 publications

(2 citation statements)

References 54 publications

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“…Prakash and Deshmukh (2011), applied an artificial immune system based fuzzy expert system to make real-time decision in flexible manufacturing system.…”

Section: Previous Workmentioning

confidence: 99%

A Simulation Study of FMS Under Routing and Part Mix Flexibility

Ali

Ahmad

2014

Glob J Flex Syst Manag

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This paper studies the performance of a flexible manufacturing system under different manufacturing strategies. Manufacturing strategies under consideration are manufacturing flexibility, lot size and scheduling of jobs in the system. The impact of all these strategies is evaluated on the make-span performance of the flexible manufacturing system. The methodologies involved include simulation, Taguchi experimental design and analyzing the results with help of ANOVA. Number of different simulation models is developed with the help of JAVA language. The first model relates to the manufacturing system, which is conventional in nature without any flexibility embedded in the system. The second model takes into partial manufacturing flexibility in the form of routing flexibility. Finally, the third models incorporate much higher level of routing flexibility. All these models are further evaluated at different level of part mix flexibility. Also the scheduling of parts is considered with the objective of minimizing the total make-span time of parts. The results show that considering different manufacturing strategies decisions in a simultaneous manner can significantly improve the performance of the flexible manufacturing systems.

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“…Prakash and Deshmukh (2011), applied an artificial immune system based fuzzy expert system to make real-time decision in flexible manufacturing system.…”

Section: Previous Workmentioning

confidence: 99%

A Simulation Study of FMS Under Routing and Part Mix Flexibility

Ali

Ahmad

2014

Glob J Flex Syst Manag

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“…This study is aimed at developing an agent-based fuzzy collaborative intelligence approach for forecasting the price of a DRAM product. Fuzzy collaborative forecasting systems for similar purposes have rarely been discussed in the literature, yet they have great potential [25][26][27][28][29][30][31][32][33]. The remainder of this paper is arranged in the following manner.…”

Section: Open Accessmentioning

confidence: 99%

An Agent-Based Fuzzy Collaborative Intelligence Approach for Predicting the Price of a Dynamic Random Access Memory (DRAM) Product

Chen

2012

Algorithms

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Predicting the price of a dynamic random access memory (DRAM) product is a critical task to the manufacturer. However, it is not easy to contend with the uncertainty of the price. In order to effectively predict the price of a DRAM product, an agent-based fuzzy collaborative intelligence approach is proposed in this study. In the agent-based fuzzy collaborative intelligence approach, each agent uses a fuzzy neural network to predict the DRAM price based on its view. The agent then communicates its view and forecasting results to other agents with the aid of an automatic collaboration mechanism. According to the experimental results, the overall performance was improved through the agents’ collaboration

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Foreign exchange rate forecasting with a virtual-expert partial-consensus fuzzy-neural approach for semiconductor manufacturers in Taiwan

Chen

2013

IJISE

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Modelling of FMS control policy: AIS-based fuzzy expert system

Cited by 4 publications

References 54 publications

A Simulation Study of FMS Under Routing and Part Mix Flexibility

A Simulation Study of FMS Under Routing and Part Mix Flexibility

An Agent-Based Fuzzy Collaborative Intelligence Approach for Predicting the Price of a Dynamic Random Access Memory (DRAM) Product

Foreign exchange rate forecasting with a virtual-expert partial-consensus fuzzy-neural approach for semiconductor manufacturers in Taiwan

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