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G/g 等候系統模擬次數分配問題之模型基礎法
| Content Provider | Semantic Scholar |
|---|---|
| Author | 張凌誠 |
| Copyright Year | 2008 |
| Abstract | Parameter setting to minimize the expected waiting time in G/G queue systems is an important issue. Regression models are constructed to describe the relationship between the expected waiting time and the parameter setting to search for the optimal setting. In the literature, Cheng and Kleijnen, Yang, Ankenman and Nelson have proposed procedures to choose setting levels needed to be simulated and the number of replication for each level. However, their models consider only one decision variable, i.e., the traffic intensity rate or the throughput rate. We propose a procedure, referred to as Model-based Computing Budget Allocation (MCBA), which combines the queuing theory and the optimum design of experiment to solve the budget allocation problem with multiple decision variables. Our approach approximates the expected waiting time with polynomial functions based on formulas developed in queuing theories and sequentially decides which parameter settings are needed to be simulated based on the concept of D-optimality. To verify the performance of MCBA, we study two cases. The first case is a G/G/1 queuing problem with the optimal parameter setting difficult to determine. The second case has an additional binary decision variable representing two different dispatching rules. Compared with the results of Optimal Computing Budget Allocation (OCBA), the proposed approach is observed to achieve higher probability of correct selection under the same simulation cost. |
| Starting Page | 1 |
| Ending Page | 67 |
| Page Count | 67 |
| File Format | PDF HTM / HTML |
| DOI | 10.6342/NTU.2008.02482 |
| Alternate Webpage(s) | http://homepages.warwick.ac.uk/~marke/research/files/jones-rawnsley.pdf |
| Alternate Webpage(s) | http://www.maths.warwick.ac.uk/~jhr/research/files/jones-rawnsley.pdf |
| Alternate Webpage(s) | https://doi.org/10.6342/NTU.2008.02482 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
