- Title
- The replicate compression heuristic for improving efficiency of urban water supply headworks optimization
- Creator
- Cui, Lijie; Kuczera, George
- Relation
- 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS. Proceedings of the 16th IAHR-APD Congress and 3rd Symposium of IAHR-ISHS (Nanjing, China 20-23 October, 2008) p. 354-360
- Relation
- http://apd-iahr2008.hhu.edu.cn/indexen.htm
- Publisher
- Tsinghua University Press
- Resource Type
- conference paper
- Date
- 2008
- Description
- Urban water supply headworks systems are usually designed to provide high security against drought. The best way to evaluate this security is to use Monte Carlo simulation which is computationally expensive. The advent of parallel computing technology in conjunction with genetic algorithms (GAS) has made it practicable to optimize operation for drought security. Nonetheless, computation turnaround times remain long. This paper presents a simple heuristic called replicate compression to improve Monte Carlo efficiency. It exploits the well known concept of a critical period. In a high reliability system there should be few critical periods. Therefore, restricting simulation to such periods should bring about substantial savings in computational effort. It was found for problems where the objective function evaluation is only affected by what happens during critical periods, replicate compression provides an effective means for substantially reducing simulation effort. The case study involving a nine-reservoir urban headworks system showed the actual reduction in effort depended on the stress experienced by the system, which in turn affected the frequency of critical periods. Even when the objective fbnction is affected by decisions outside the critical period, replicate compression may provide a useful result by helping to guide the specification of a reduced search space for the genetic algorithm. This strategy can bring about substantial savings in turnaround time.
- Subject
- drought security; optimization; Monte Carlo; critical period; genetic algorithms
- Identifier
- uon:6228
- Identifier
- http://hdl.handle.net/1959.13/802868
- Identifier
- ISBN:9787894742346
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