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

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.