Heuristic approaches for the solution of high-dimensional stochastic routing and scheduling problems

在运筹学(OR)所涉及的网络中优化流量或路线的基础上，可以分析和很好地解决许多问题。然而，随机因素在很大程度上是网络问题中的主要因素，而目前的研究并不总是考虑这些因素。实际上，在解决有限资源必须以最佳方式分配的实际问题时，特别是需要充分考虑整个过程的不确定性，以及根据最新的现有信息在不同的时间点及时做出一系列决策。换句话说，它可以被定义为可以被描述为马尔可夫决策过程(MDP)的动态问题，但状态空间可能是高维的，我们可能需要彻底考虑诸如近似动态规划(DP)或强化学习等方法。总而言之，我们的目标是为这类问题设计有效的启发式算法，并证明它们比可能考虑的替代方案执行得更好。

Many problems can be analysed and well solved on the basis of optimising flows or routes in networks involved in operations research (OR). However, stochastic factors largely take a major part in network problems, which are not always considered in nowadays research. Actually, when solving practical problems in which limited resources must be distributed in an optimal way, uncertainty across the whole process and making a series of decisions at different points timely according to the latest available information in particular need to be fully taken into consideration. In other words, it can be defined as dynamic problems that can be formulated as a Markov decision process (MDP), but the state space may be high-dimensional and we likely need to consider approaches such as approximate dynamic programming (DP) or reinforcement learning thoroughly. In conclusion, we aim to devise efficient heuristics for these kinds of problems and demonstrate that they perform better than alternatives that might be considered.