For the Electric Vehicle Routing Problem with Time Windows, Deep Reinforcement Learning PROJECT TITLE : Deep Reinforcement Learning for the Electric Vehicle Routing Problem with Time Windows ABSTRACT: The past ten years have witnessed a significant increase in the number of electric vehicles (EVs) on the road as an increasing number of businesses in the transportation and logistics industries have begun to utilize electric vehicles (EVs) for the delivery of their services. We make use of the electric vehicle routing problem with time windows in order to simulate the workings of a commercial electric vehicle fleet (EVRPTW). In this paper, we present a framework for end-to-end deep reinforcement learning, which we propose can solve the EVRPTW. In particular, we develop an attention model that incorporates the pointer network as well as a graph embedding layer in order to parameterize a stochastic policy for the purpose of solving the EVRPTW. After that, the model is trained by utilizing policy gradient in conjunction with rollout baseline. The results of our numerical studies indicate that the proposed model is able to solve EVRPTW instances of large sizes in an efficient manner, which are not solvable with the approaches that are currently in use. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Deep Clustering for Skin Lesion Detection in Highly Imbalanced Datasets via Center-Oriented Margin Free-Triplet Loss For Intelligent Transportation Systems, Deep Reinforcement Learning: A Survey
