Please use this identifier to cite or link to this item:
|Title:||Optimal computing budget allocation for multi-objective simulation models|
|Authors:||Lee, L.H. |
|Source:||Lee, L.H.,Chew, E.P.,Teng, S.,Goldsman, D. (2004). Optimal computing budget allocation for multi-objective simulation models. Proceedings - Winter Simulation Conference 1 : 586-593. ScholarBank@NUS Repository.|
|Abstract:||Simulation plays a vital role in identifying the best system design from among a set of competing designs. To improve simulation efficiency, ranking and selection techniques are often used to determine the number of simulation replications required so that a pre-specified level of correct selection is guaranteed at a modest possible computational expense. As most real-life systems are multi-objective in nature, in this paper, we consider a multi-objective ranking and selection problem, where the system designs are evaluated in terms of more than one performance measure. We incorporate the concept of Pareto optimality into the ranking and selection scheme, and try to find all of the non-dominated designs rather than a single "best" one. A simple sequential solution method is proposed to allocate the simulation replications. Computational results show that the proposed algorithm is efficient in terms of the total number of replications needed to find the Pareto set.|
|Source Title:||Proceedings - Winter Simulation Conference|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.