65 citations · 111 across the 9 of their papers we have counts for
6 papers · 1 filter
Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach
Stephen Mak, Liming Xu, Tim Pearce +2
Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. This ac…
Coalitional Bargaining via Reinforcement Learning: An Application to Collaborative Vehicle Routing
Stephen Mak, Liming Xu, Tim Pearce +2
Collaborative Vehicle Routing is where delivery companies cooperate by sharing their delivery information and performing delivery requests on behalf of each other. This achieves ec…
On Implementing Autonomous Supply Chains: a Multi-Agent System Approach
Liming Xu, Stephen Mak, Maria Minaricova +1
Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore th…
Multi-Agent Digital Twinning for Collaborative Logistics: Framework and Implementation
Liming Xu, Stephen Mak, Stefan Schoepf +2
Collaborative logistics has been widely recognised as an effective avenue to reduce carbon emissions by enhanced truck utilisation and reduced travel distance. However, stakeholder…
Using Reinforcement Learning for the Three-Dimensional Loading Capacitated Vehicle Routing Problem
Stefan Schoepf, Stephen Mak, Julian Senoner +3
Heavy goods vehicles are vital backbones of the supply chain delivery system but also contribute significantly to carbon emissions with only 60% loading efficiency in the United Ki…
Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogates
Kyle Mana, Fernando Acero, Stephen Mak +4
Discrete optimization belongs to the set of -hard problems, spanning fields such as mixed-integer programming and combinatorial optimization. A current standard appro…