An intelligent decision support system prototype for hinterland port logistics
作者:
Highlights:
• We simulate a prototype of an intelligent decision support tool for port logistics.
• A multi-agent simulation with optimisation algorithm enables heterogeneous actions.
• We present a reinforcement learning, inspired by human decision-making strategies.
• The simulation demonstrates the dynamics of delivery and pick up of shipments.
• Reinforcement learning shows the DSS is attractive for smaller freight agents.
摘要
Port logistics is characterised by a high degree of fragmentation, uncertainty and complexity. In a context with these characteristics, decision support can be of significant value. This study presents the prototype of an intelligent decision support system (DSS) that leads to horizontal and vertical cooperation among freight agents involved in port logistics. A multi-agent simulation model is presented where heterogeneous actions are enabled as a result of an adaptive reinforcement learning algorithm that is inspired by human decision-making strategies. The model combines optimisation modelling and decision theory to operate in a dynamic environment characterised by information asymmetry among agents and dynamic changes over time. A simulation demonstrates the dynamics and convergence to equilibrium of the interactions of heterogeneous agents for delivery and pick up of import/export shipments.
论文关键词:Agent-based model,Port community system,Vehicle routing problem,Reinforcement learning,Freight transportation
论文评审过程:Received 13 May 2019, Revised 26 November 2019, Accepted 26 November 2019, Available online 3 December 2019, Version of Record 31 January 2020.
论文官网地址:https://doi.org/10.1016/j.dss.2019.113227