Robust and stochastic multistage optimisation under Markovian uncertainty with applications to production/inventory problems
研究了一类马尔可夫不确定性下的多阶段生产/库存优化问题，提出了构建状态空间表示的不确定集的方法，并比较了鲁棒与随机优化策略的风险表现，发现鲁棒优化在低风险水平（低于2%）时显著更优。
A generic class of multistage optimisation problems related to production/inventory management under Markovian uncertainty is introduced and investigated. For each instance in the class, it is shown how to construct state-space representable uncertainty sets at any probability level, thus leading to efficient resolution of both the stochastic and robust versions of the problem. Computational experiments aimed at comparing the optimal strategies corresponding to both versions in terms of risk are then reported and discussed; it is observed that the robust optimisation approach can significantly outperform the stochastic optimisation approach when targeting lower risk levels (typically less than 2%).