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solution space. ▫ Solution traces to reduce searching. ▫ Keeping track of past searches. ▫ Keeping track of past s
Handling Large Datasets in Parallel Metaheuristics: A Spares Management p Case Study y and Optimization

Chee Shin Yeo, Elaine Wong Kay Li, Yong Siang Foo

Metaheuristics 





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Solve optimization problems in diverse domains Search over a solution space for an optimal solution that will minimise an objective function Challenges  Exponentially increasing execution time  Memory intensive  Inconsistent performance due to random generation

Parallel Metaheuristics 







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Search in parallel using multiple searches over a solution space Solution traces to reduce searching  Keeping track of past searches Cooperative methods with different initial solutions  Parallel searches exchange intermediate results Problem: Large datasets  Insufficient Memory  Network bottlenecks

Parallel Metaheuristics: Flow Control Workflow 



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Run as a flow control workflow with n states, states each state with x Multiple Independent Runs (MIR)

Optimal solution is the output On of the last state (Sn)

Flow Control Workflow: Clustering Policy Workflow Clustering Policy: Executes the entire workflow as a single job

S1 MIR1a MIR1b

Processor P2 P1

Td

S2 MIR2a MIR2b

S3 MIR3a MIR3b

entire workflow

Ta

Time

State Clustering Policy: Executes each state of the entire workflow as a single g jjob

S1 MIR1a MIR1b

Processor P2 P1

Td

S1

S2 MIR2a MIR2b

Ta Td

S2

S3 MIR3a MIR3b

Ta Td

S3

Ta

Time

Job Clustering Policy: Executes each MIR in a state as a single job

S1 MIR1a MIR1b

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Processor P2 P1

S2 MIR2a MIR2b

S3 MIR3a MIR3b

Td MIR1a Ta Td MIR2a Ta Td MIR3a Ta Td MIR1b Ta Td MIR2b Ta Td MIR3b Ta

Time

Case Study: Spares Management and Optimization (SMO) 

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Optimization scenario  Aircraft spare parts  59 airports – with time time-based based delivery commitment at selective airports  Logistics flights between all locations  Flight network – ~320,000 Flight Hours/year

Experimental Setup

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Effect of Stop Criterion on IBM (2 (2.26GHz, 26GHz 32GB)

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Effect of Clustering Policy on DELL (3 (3.0GHz, 0GHz 4GB)

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Conclusion 



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Flow control workflow for p parallel metaheuristics  Stop Criterion: Exchange of intermediate data  Clustering g Policy: y Assignment g of jjobs Memory y availability y is a critical issue  Less iterations for stop criterion is better  Less memory, shorter completion time  Job clustering policy is better  Least memory, memory more reliable completion

Future Work 



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More intelligent g optimization p  Self-configuring stop criterion Resource contention in multi-user environment  Effective scheduling g mechanism

End of Presentation Thank You Any Questions/Comments? 

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