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Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach
Our contribution is that we are the first to consider both the route allocation problem and gain sharing problem simultaneously - without access to the expensive characteristic function.
Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing
Our contribution is that our decentralised approach is both scalable and considers the self-interested nature of companies.
Towards Autonomous Supply Chains: Definition, Characteristics, Conceptual Framework, and Autonomy Levels
Recognising that this work represents an initial endeavour, we emphasise the need for continued exploration in this emerging domain.
Unlocking Carbon Reduction Potential with Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
Our model not only represents a promising first step towards large-scale logistics optimisation with reinforcement learning but also lays the foundation for this research stream.
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems, spanning fields such as mixed-integer programming and combinatorial optimization.
Will bots take over the supply chain? Revisiting Agent-based supply chain automation
For example, the ubiquity of IoT technology helps agents "sense" the state of affairs in a supply chain and opens up new possibilities for automation.
