Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency
arXiv:2607.06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility. In this study, we address a dynamic, online variant combining elements of the VRP and the Orienteering Problem (OP), in which a fleet of vehicles must maximise cumulative reward collected within a fixed time horizon while continuously replanning as new tasks arrive. We propose and evaluate a reward-density heuristic for dynamic multi-vehicle assignment, referred to as the Efficienc
The paper leverages new computational capabilities and a heightened demand for dynamic logistics optimization, driven by the increasing complexity of supply chains and urban mobility.
This development pushes the frontier of autonomous decision-making in logistics, potentially enabling more efficient and resilient supply chains that adapt dynamically to real-time events.
The ability to dynamically re-plan multi-vehicle routes for maximum reward collection represents a significant step towards fully autonomous and adaptive logistics systems.
- · Logistics companies
- · E-commerce
- · Urban delivery services
- · AI software providers
- · Traditional logistics planning software
- · Companies with rigid delivery models
Improved efficiency and cost reduction in delivery and service industries due to optimized routing and real-time adaptation.
Increased reliance on AI-driven dispatch and fleet management, potentially leading to fewer human dispatchers and drivers, or a shift in their roles.
Enhanced urban mobility management through integrated dynamic routing that optimizes for both reward (delivery efficiency) and congestion, creating 'smart city' logistical networks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI