Smart Truck

Smart Truck

The special real-time problem of transport resources allocation for freight transportation companies that deliver cargo via FTL business model was considered. Each freight transportation company should react on incoming events adaptively reallocate available resources. For this purposes multi agent systems are well proved and used in many modern freight companies. But it was admitted that there is a possibility to improve a quality optimization level by using classic optimization approach in the special initial allocation subproblem. By using expert human real logistic scheduling knowledge for a long time period the essential set of limitations to this initial allocation plan problem was defined. The problem was formalized similar to the classical assignment problem of linear programming. Acyclic and cyclic cases of the problem were considered. It was shown that the acyclic case of the problem could be reduced to the assignment problem easily but for the cyclic case it requires to exclude important resource to order matching condition. Finding the exact solution of the initial plan problem was proposed by using the Hungarian method, which is well proved exact method. It was also shown that this method couldn’t be applied in case of real time optimization, because even in static cyclic case of the problem it is impossible to support resource to order matching condition for next future orders, but it can be applied as an addition to the multi agent approach.

The article describes main functionalities and developing principles of intellectual management system for consolidated cargoes within intra-regional deliveries. The application of adaptive scheduling mechanism for flexible solving the logistic problems is considered.

The use o f multi-agent platform for real-time adaptive scheduling o f trucks is considered. The schedule in such system is formed dynamically by balancing the interests o f orders and resource agents. The system doesn’t stop or restart to rebuild the plan o f 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 o f cargo transportation for truck companies having own fleet are analyzed based on simulation o f statistically representative flows o f 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 o f 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 o f using the multi-agent systems with real-time decision making - up to 40-60% comparing with rigid models. The profit dependencies on the number o f 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%).

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.

Исследованы различные модели организации грузовых перевозок для компании с собственным парком грузовиков - от наиболее жестких, связанных с возвращением грузовиков на базу после каждой поездки, к более гибким, связанным с ожиданием заказов в пунктах разгрузки, разрешением грузовикам опаздывать с введением штрафов за опоздание и адаптивным перепланированием заказов в реальном времени. Полученные графики изменения прибыли во времени для каждой модели, позволяющие на практике показать и оценить преимущества перехода к принятию решений в реальном времени. Исследованы зависимости прибыли компании от числа грузовиков.

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