Reference architecture for holonic manufacturing systems: PROSA
Computers in Industry - Special issue on manufacturing systems
Recent Trends in Logistics and the Need for Real-Time Decision Tools in the Trucking Industry
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 3 - Volume 3
Constraint Processing
Magenta technology multi-agent logistics i-Scheduler for road transportation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Welfare engineering in practice: on the variety of multiagent resource allocation problems
ESAW'04 Proceedings of the 5th international conference on Engineering Societies in the Agents World
An interactive platform for auction-based allocation of loads in transportation logistics
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track
Adaptive Planning for Supply Chain Networks
HoloMAS '07 Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
An Evaluation Framework for MAS Modeling Languages Based on Metamodel Metrics
Agent-Oriented Software Engineering IX
A multi-agent platform for auction-based allocation of loads in transportation logistics
Expert Systems with Applications: An International Journal
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Automation and Remote Control
| Hi-index | 0.00 |
The paper describes functionality of Magenta Multi-Agent Logistics i-Scheduler Engine presented on AAMAS 2006 conferences and gives examples of its application in business domain. The i-Scheduler Engine was designed to be scalable without risk of combinatorial explosion, in order to handle large transportation networks as a whole. The multi-agent architecture combined with semantic network allows very granular approach for every business entity of transportation network (client, order, cargo, truck, driver, etc) and balancing of their conflicting interests. The i-Scheduler considers individual constraints and, interestingly, specific preferences of customers, drivers, trucks, cargoes, etc. This results in a unique ability to combine inbound and outbound deliveries, different fleets or private networks, driving more value from finding effective backhauls and consolidations. The paper covers the history of development, architecture and current functionality of the engine and provides a set of case studies in different transportation networks, which outline the most serious challenges Magenta overcame in each case.