Sequential Learning with a Similarity Selection Index
针对大规模仿真优化中无法穷举所有方案的问题，提出一种新的相似性模型，利用方案间的相似性从少量样本中学习更多信息，以改进最终选择决策。
In large-scale simulation optimization, it is impossible to exhaustively simulate every choice. However, there are often inherent similarities between choices: for example, two similar sets of input settings to a simulation model can reasonably be expected to produce similar output. The information gained from simulating one choice can thus be used to infer the values of other similar choices, enabling learning more from a relatively small number of samples. The paper “Sequential Learning with a Similarity Selection Index,” by Zhou, Fu, and Ryzhov, develops a new similarity model to improve the final selection decision after all samples have been collected. The new “similarity indices” are complementary to all existing information collection procedures, which do not focus on the final decision. At the same time, the new model allows a tractable theoretical treatment of an optimal procedure, which can be efficiently approximated.