Authors:
Oleg Granichin
1
;
Petr Skobelev
2
;
Alexander Lada
3
;
Igor Mayorov
3
and
Alexander Tsarev
3
Affiliations:
1
SPbSU, Russian Federation
;
2
Software Engineering Company «Smart Solutions» and Ltd., Russian Federation
;
3
Smart Solutions and Ltd, Russian Federation
Keyword(s):
Multi-agent Systems, Adaptive Scheduling, Trucks, Cargo Transportation, Simulation, Real-Time, Mobile Resources.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Collective Intelligence
;
Distributed and Mobile Software Systems
;
Distributed Problem Solving
;
Enterprise Information Systems
;
Formal Methods
;
Group Decision Making
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Planning and Scheduling
;
Simulation and Modeling
;
Software Engineering
;
Symbolic Systems
;
Task Planning and Execution
Abstract:
The use of multi-agent platform for real-time adaptive scheduling of trucks is considered. The schedule in such system is formed dynamically by balancing the interests of orders and resource agents. The system doesn’t stop or restart to rebuild the plan of mobile resources in response to upcoming events but finds out conflicts and adaptively re-schedule demand-resource links in plans when required. Different organizational models of cargo transportation for truck companies having own fleet are analyzed based on simulation of statistically representative flows of orders. Models include the rigid ones, where trucks return back to their garage after each trip, and more flexible, where trucks wait for new orders at the unloading positions, where trucks can be late but pay a penalty for this, and finally where orders can be adaptively rescheduled ’on the fly‘ in real-time and the schedule of each truck can change individually during orders execution. Results of simulations of trucks prof
it depending on time period are presented for each model. These results show measurable benefits of using the multi-agent systems with real-time decision making - up to 40-60% comparing with rigid models. The profit dependencies on the number of trucks are also built and analyzed. The results show that using adaptive scheduling in real time it is possible to execute the same number of orders with less trucks (up to 20%).
(More)