A Two-stage Learning-based method for Large-scale On-demand pickup and delivery services with soft time windows
With the rapid growth of the on-demand logistics industry, large-scale pickup and delivery
with soft time windows has become widespread in various time-critical scenarios. This …
with soft time windows has become widespread in various time-critical scenarios. This …
Solve routing problems with a residual edge-graph attention neural network
For NP-hard combinatorial optimization problems, it is usually challenging to find high-
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …
quality solutions in polynomial time. Designing either an exact algorithm or an approximate …
Learning to solve vehicle routing problems: A survey
This paper provides a systematic overview of machine learning methods applied to solve NP-
hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both …
hard Vehicle Routing Problems (VRPs). Recently, there has been a great interest from both …
Deep reinforcement learning of passenger behavior in multimodal journey planning with proportional fairness
Multimodal transportation systems require an effective journey planner to allocate multiple
passengers to transport operators. One example is mobility-as-a-service, a new mobility …
passengers to transport operators. One example is mobility-as-a-service, a new mobility …
[HTML][HTML] A new hyper-heuristic based on adaptive simulated annealing and reinforcement learning for the capacitated electric vehicle routing problem
Electric vehicles (EVs) have been adopted in urban areas to reduce environmental pollution
and global warming due to the increasing number of freight vehicles. However, there are still …
and global warming due to the increasing number of freight vehicles. However, there are still …
A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem
Maintenance planning aims to improve the reliability of assets, prevent the occurrence of
asset failures, and reduce maintenance costs associated with downtime of assets and …
asset failures, and reduce maintenance costs associated with downtime of assets and …
Towards microgrid resilience enhancement via mobile power sources and repair crews: A multi-agent reinforcement learning approach
Mobile power sources (MPSs) have been gradually deployed in microgrids as critical
resources to coordinate with repair crews (RCs) towards resilience enhancement owing to …
resources to coordinate with repair crews (RCs) towards resilience enhancement owing to …
A reinforcement learning approach for rebalancing electric vehicle sharing systems
This paper proposes a reinforcement learning approach for nightly offline rebalancing
operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse …
operations in free-floating electric vehicle sharing systems (FFEVSS). Due to sparse …
Intelligent decision-making and human language communication based on deep reinforcement learning in a wargame environment
The application of artificial intelligence (AI) in games has been significantly developed and
attracted much attention over the past few years. This article not only leverages the …
attracted much attention over the past few years. This article not only leverages the …
Transformer-based reinforcement learning for pickup and delivery problems with late penalties
Pickup and delivery problems with late penalties can be adopted to model a wide range of
practical situations in the field of transportation and logistics. However, the restrictions on the …
practical situations in the field of transportation and logistics. However, the restrictions on the …