期刊文献+
共找到2篇文章
< 1 >
每页显示 20 50 100
Modelling to Generate Alternatives Using Simulation-Driven Optimization: An Application to Waste Management Facility Expansion Planning
1
作者 Julian Scott Yeomans Raha Imanirad 《Applied Mathematics》 2012年第10期1236-1244,共9页
Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting deci... Public sector decision-making typically involves complex problems that are riddled with competing performance objecttives and possess design requirements which are difficult to capture at the time that supporting decision models are constructed. Environmental policy formulation can prove additionally complicated because the various system components often contain considerable stochastic uncertainty and frequently numerous stakeholders exist that hold completely incompatible perspectives. Consequently, there are invariably unmodelled performance design issues, not apparent at the time of the problem formulation, which can greatly impact the acceptability of any proposed solutions. While a mathematically optimal solution might provide the best solution to a modelled problem, normally this will not be the best solution to the underlying real problem. Therefore, in public environmental policy formulation, it is generally preferable to be able to create several quantifiably good alternatives that provide very different approaches and perspectives to the problem. This study shows how a computationally efficient simulation-driven optimization approach that com- bines evolutionary optimization with simulation can be used to generate multiple policy alternatives that satisfy required system criteria and are maximally different in decision space. The efficacy of this modelling-to-generate-alternatives method is specifically demonstrated on a municipal solid waste management facility expansion case. 展开更多
关键词 simulation-driven OPTIMIZATION Modelling-to-Generate-Alternatives Environmental DECISION Making under Uncertainty
下载PDF
Optimization of Linear Filtering Model to Predict Post-LASIK Corneal Smoothing Based on Training Data Sets
2
作者 Anatoly Fabrikant Guang-Ming Dai Dimitri Chernyak 《Applied Mathematics》 2013年第12期1694-1701,共8页
Laser vision correction is a rapidly growing field for correcting nearsightedness, farsightedness as well as astigmatism with dominating laser-assisted in situ keratomileusis (LASIK) procedures. While the technique wo... Laser vision correction is a rapidly growing field for correcting nearsightedness, farsightedness as well as astigmatism with dominating laser-assisted in situ keratomileusis (LASIK) procedures. While the technique works well for correcting spherocylindrical aberrations, it does not fully correct high order aberrations (HOAs), in particular spherical aberration (SA), due to unexpected induction of HOAs post-surgery. Corneal epithelial remodeling was proposed as one source to account for such HOA induction process. This work proposes a dual-scale linear filtering kernel to model such a process. Several retrospective clinical data sets were used as training data sets to construct the model, with a downhill simplex algorithm to optimize the two free parameters of the kernel. The performance of the optimized kernel was testedon new clinical data sets that were not previously used for the optimization. 展开更多
关键词 simulation-driven OPTIMIZATION DOWNHILL SIMPLEX Method CORNEAL SMOOTHING LASIK
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部