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Talking Trucks dataset (v2021.12) This dataset is used for research into Self-organizing logistics and published as part of the paper "Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics". It is managed by the TNO Sustainable Transport and Logistics department. Please contact Christian van Ommeren (christian.vanommeren@tno.nl) and Ruben Fransen (ruben.fransen@tno.nl) for details. The dataset contains 4 types of objects (files): agents, orders, route stops, and locations; and currently holds data for three experiments, represented by a UUIDv4 identifier. Agents Represents an agent that is able to transport load and is to be assigned to specific orders. This file contains the agent's unique UUIDv4 identifier; agent type; Euronorm emission norm; agent cost for driving a km in EUR; agent cost per hour in EUR; working hours start and end times; and agent starting coordinates. Orders Represents the unit that should be transported by the agents. This file contains the order's unique UUIDv4 identifier; container type; TEU; trade type (import/export); and weight. Route stops Represents the stop which is associated with an order. Stops are tied to a real-world location. This file contains the route stop's unique UUIDv4 identifier; stop index; location UUIDv4 identifier; type (origin/destination); order UUIDv4 identifier; and time window times. Locations Represents a real-world location at which logistics operations take place (for a specific duration). This file contains the location's unique UUIDv4 identifier; country designator; coordinates; Euronorm emission norm; stop duration; and service type (decoupling, live handling, craning, ...). License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Self-organizing Logistics, Container scheduling, Collaborative Decision-making, Multi-agent systems, Decentralized Decision-making, DCOP, Logistics, Distributed Constraint Optimization, Reinforcement Learning, Digital Twins, Order scheduling
Self-organizing Logistics, Container scheduling, Collaborative Decision-making, Multi-agent systems, Decentralized Decision-making, DCOP, Logistics, Distributed Constraint Optimization, Reinforcement Learning, Digital Twins, Order scheduling
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