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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 reschedule 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 profit 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%).

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The application of multi-agent platform for real-time adaptive scheduling of trucks is considered. In case of unpredictable events the system works adaptively and doesn't stop to restart the plan from the beginning. Different models of cargo transportation for truck companies having own fleet are analysed. The results show that using adaptive scheduling in real time it is possible to create significantly more profitable schedules (up to 40-60% compared with rigid models) and save a number of trucks (up to 20%) for the same amount of orders.

No abstract