A Graph Theoretic Approach to Non-Anticipativity Constraint Generation in Multistage Stochastic Programs with Incomplete Scenario Sets

A Node Formulation for Multistage Stochastic Programs with Endogenous Uncertainty

This paper introduces a node formulation for multistage stochastic programs with endogenous (i.e., decision-dependent) uncertainty. Problems with such structure arise when the choices of the decision maker determine a change in the likelihood of future random events. The node formulation avoids an explicit statement of non-anticipativity constraints, and as such keeps the dimension of the model sizeable. An exact solution algorithm for a special case is introduced and tested on a case study. Results show that the algorithm outperforms a commercial solver as the size of the instances increases.

融合超度量Gromov-Wasserstein距离用于多阶段随机规划中的场景树生成

Fused ultrametric Gromov-Wasserstein for Scenario Tree generation in Multistage Stochastic Programming

本文提出融合超度量Gromov-Wasserstein与融合Gromov-Wasserstein距离，用于多阶段随机规划场景树生成，同时保证边际保真度和非预期性，并给出稳定性定理及块坐标下降算法。

Optimal Scheduling and Exact Response Time Analysis for Multistage Jobs

Scheduling to minimize mean response time in an M/G/1 queue is a classic problem. The problem is usually addressed in one of two scenarios. In the perfect-information scenario, the scheduler knows each job's exact size, or service requirement. In the zero-information scenario, the scheduler knows only each job's size distribution. The well-known shortest remaining processing time (SRPT) policy is optimal in the perfect-information scenario, and the more complex Gittins policy is optimal in the zero-information scenario. In real systems the scheduler often has partial but incomplete information…