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Chung, Yun-Kung

doi:10.1023/a:1008980812648

Cited by 21 publications

(2 citation statements)

References 59 publications

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“…Deploying WMNs in metropolitan areas has a number of advantages (Holland et al, 1975;Chung et al, 1999). The physical layer provides a higher average transmission to any cellular network and need not depend on a wiring.…”

Section: Metropolitan Area Networkmentioning

confidence: 99%

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

Barolli

Takizawa

Xhafa

et al. 2012

International Journal of Distributed Systems and Technologies

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In this paper, the authors propose and implement a system based on Genetic Algorithms (GAs) called WMN-GA. They evaluated the performance of WMN-GA for 0.7 crossover rate and 0.3 mutation rate, exponential ranking and different distribution of clients considering size of giant component and number of covered users parameters. The simulation results show that for normal distribution the system has better performance. The authors also carried out simulations for 0.8 crossover rate and 0.2 mutation rate. The simulation results show that the setting for 0.7 crossover rate and 0.3 mutation rate offers better connectivity and user coverage.

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Section: Metropolitan Area Networkmentioning

confidence: 99%

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

Barolli

Takizawa

Xhafa

et al. 2012

International Journal of Distributed Systems and Technologies

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“…Although not rooted in mathematical programming, these random search techniques have proven to be powerful techniques to solve many combinatorial problems, including the layout problem, rather efficiently. Artificial intelligence techniques such as expert systems have also been used to solve some types of layout problems efficiently (Heragu and Kusiak 1990, Ngoi Kok Ann and Chua 1994, Chung 1999). A detailed review of the layout problem can be found in Kusiak and Heragu (1987).…”

Section: Literature Reviewmentioning

confidence: 99%

Reconfigurable layout problem

Meng¹,

Heragu

Zijm

2004

International Journal of Production Research

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This paper addresses the reconfigurable layout problem, which differs from traditional, robust and dynamic layout problems mainly in two aspects: first, it assumes that production data are available only for the current and upcoming production period. Second, it considers queuing performance measures such as work in progress inventory and product lead time in the objective function of the layout problem. A process to solve the reconfigurable layout problem is proposed. A previously developed, open queuing network-based analytical model, called Manufacturing system Performance Analyser, is used to estimate the stochastic performance measures of a layout. These are combined with deterministic performance measures such as material handling cost to determine the layout for the next period. A case study is used to illustrate the process of solving the reconfigurable layout problem. Revision

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Learning from Prior Designs for Facility Layout Optimization

Rummukainen¹,

Nurminen

Syrjänen

et al. 2020

Heuristics for Optimization and Learning

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The problem of facility layout involves not only optimizing the locations of process components on a factory floor, but in real-world applications there are numerous practical constraints and objectives that can be difficult to formulate comprehensively in an explicit optimization model. As an alternative to explicit modelling, we present an optimization approach that learns structural properties from examples of expert-designed layouts of other similar facilities, and considers similarity to the examples as one objective in a multiobjective facility layout optimization problem. We have tested the approach on small-scale artificial test data, and the initial results indicate that a layout objective can be learned from example layouts, even if the process structure in the examples differs from the target case.

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Untitled

Cited by 21 publications

References 59 publications

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

Performance Evaluation of WMN-GA Simulation System for Different Settings of Genetic Operators Considering Giant Component and Number of Covered Users

Reconfigurable layout problem

Learning from Prior Designs for Facility Layout Optimization

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