Search Results - (( development lean selection algorithm ) OR ( java loading optimization algorithm ))
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1
Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…This research proposes an enhancement of the ant colony optimization algorithm that caters for dynamic scheduling and load balancing in the grid computing system. …”
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2
Development of a systematic method in lean tool selection for automotive industry
Published 2012“…The developed software probably assists manufacturers in applying the systematic lean tool selection. …”
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3
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. …”
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4
Developing Android Application To Guide Lean Six Sigma PDCA Project
Published 2018“…Massachusetts Institute of Technology (MIT) App Inventor was selected as the application development platform. Guidelines to PDCA, including the associated lean tools for different stages were collected from the literature review. …”
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Monograph -
5
Resource management in grid computing using ant colony optimization
Published 2011“…Resources with high pheromone value are selected to process the submitted jobs.Global pheromone update is performed after completion processing the jobs in order to reduce the pheromone value of resources.A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against other ant based algorithm, in terms of resource utilization.Experimental results show that EACO produced better grid resource management solution.…”
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6
Implementation of locust inspired scheduling algorithm with huge number of servers for energy efficiency in a cloud datacenter
Published 2019“…Cloudsim is used as Discrete Event Simulation tool and Java as coding language to evaluate LACE algorithm. …”
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7
Energy-driven scheduling for Digital Video Broadcasting - Satellite Second Generation (DVB-S2)
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8
Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…The final optimum learning algorithm was selected based on the performance values (regression…”
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