样本均值近似(sample average approximation)方法经典论文及论点汇总

目录

1. 关于SAA基础原理的描述

1.1 论点1

1.1.1 文献来源

1.1.2 论点详述

1.1.3 推荐文献

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1. 关于SAA基础原理的描述

1.1 论点1

1.1.1 文献来源

He, Y., Qi, M., Zhou, F., & Su, J. (2020). An effective metaheuristic for the last mile delivery with roaming delivery locations and stochastic travel times. Computers & Industrial Engineering, 145, 106513.

1.1.2 论点详述

The basic idea of SAA is that the stochastic problem is solved repeatedly M replications, each of which generates a random sample of size N and then obtains the optimal solution as well as expected objective value. The solution is evaluated by the optimality gap to the ”true” solution based on a large sample of size N′. If the optimality gap are sufficiently small, we accept the solution.

1.1.3 推荐文献

1. Mak, W. K., Morton, D. P., & Wood, R. K. (1999). Monte Carlo bounding techniques for determining solution quality in stochastic programs. Operations research letters, 24(1-2), 47-56.
2. Kleywegt, A. J., Shapiro, A., & Homem-de-Mello, T. (2002). The sample average approximation method for stochastic discrete optimization. SIAM Journal on optimization, 12(2), 479-502.
3. Li, B., Krushinsky, D., Van Woensel, T., & Reijers, H. A. (2016). The share-a-ride problem with stochastic travel times and stochastic delivery locations. Transportation Research Part C: Emerging Technologies, 67, 95-108.
4. Verweij, B., Ahmed, S., Kleywegt, A. J., Nemhauser, G., & Shapiro, A. (2003). The sample average approximation method applied to stochastic routing problems: a computational study. Computational optimization and applications, 24, 289-333.