In practically all real-world planning and scheduling problems one is faced with uncertainty. Usually this uncertainty is abstracted away during the modeling process, such that standard combinatorial optimization algorithms can be used to solve the problem. This abstraction step is necessary, because these algorithms generally cannot deal with uncertainty. In this thesis we study the Vehicle Ro...

The past decade has seen a rapid penetration of electric vehicles (EVs) as more and logistics transportation companies start to deploy for service provision. In order model the operations commercial EV fleet, we utilize routing problem with time windows (EVRPTW). this paper, propose an end-to-end deep reinforcement learning framework solve EVRPTW. particular, develop attention incorporating poi...

The Vehicle Routing Problem with Simultaneous Pickup-Delivery and Time Windows (VRPSPDTW) has attracted much research interest in the last decade, due to its wide application modern logistics. Since VRPSPDTW is NP-hard exact methods are only applicable small-scale instances, heuristics meta-heuristics commonly adopted. In this paper we propose a novel Memetic Algorithm efficienT local search Ex...