Current position: Home >> Scientific Research >> Paper Publications

A Double-Agent Neighbor-State Q-learning Algorithm for Dynamic Scheduling Twin-ASCs in ACTs

Release Time:2024-06-28  Hits:

Date of Publication: 2024-05-25

Journal: ACM International Conference Proceeding Series

Page Number: 1-7

Key Words: ASC scheduling; Automated container terminals; Automated stacking cranes; Automation; Containers; Container transportation; Double agents; Dynamic scheduling; Dynamic scheduling problems; Learning algorithms; Markov decision process models; Markov processes; Port terminals; Q-learning algorithms; Railroad yards and terminals; Reinforcement learnings; Scheduling algorithms; Software agents

Prev One:A Simulation Optimization Approach to Optimize Dredger Fleet Schedule Plan for Dredging Works in Existing Ports

Next One:Power Balancing Optimization for Reefer Yards Considering Uncertainties