Abstract | ||
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The paper describes main features of an Intelligent Scheduler for Road Transportation Applications based on Magenta agent technology and characterized by a number of unique, advanced features such as real-time, incremental, continuous scheduling and schedule improvement, scalability from small to very large enterprise networks, multi-criteria schedule analysis and rich decision support for the user, pro-active agent negotiations.The scheduler is event driven, which means that it construct schedules taking into account the occurrence of all schedule affecting events (such as the arrival of a new order, order cancellation, truck failure, road unavailability and driver no-show). As an event occurs the scheduler rapidly re-schedules only the affected part of the schedule thus causing the minimum disruption to already allocated resources.The Scheduler has been tested on real data supplied by several UK Road Logistics operators.The Scheduler was developed using protected Magenta technology for agent negotiations in virtual environment and knowledge discovery. |
Year | DOI | Venue |
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2006 | 10.1145/1160633.1160927 | AAMAS |
Keywords | Field | DocType |
pro-active agent negotiation,uk road logistics operator,road transportation,new order,road transportation applications,magenta agent technology,agent negotiation,magenta technology,magenta technology multi-agent logistics,schedule improvement,intelligent scheduler,multi-criteria schedule analysis,real time,microeconomics,resource allocation,virtual environment,decision support,knowledge discovery,multi agent system,multi agent systems | Virtual machine,Computer science,Scheduling (computing),Decision support system,Multi-agent system,Schedule,Unavailability,Knowledge extraction,Scalability,Distributed computing | Conference |
ISBN | Citations | PageRank |
1-59593-303-4 | 13 | 1.50 |
References | Authors | |
1 | 3 |
Name | Order | Citations | PageRank |
---|---|---|---|
Jonathan Himoff | 1 | 13 | 2.17 |
George Rzevski | 2 | 69 | 12.37 |
Petr Skobelev | 3 | 118 | 24.61 |