Framework of Optimization of Transport Process with Use of Intelligent Hybrid System
[ 1 ] Katedra Zarządzania Produkcją i Logistyki, Wydział Inżynierii Zarządzania, Politechnika Poznańska | [ P ] employee
2013
paper
english
- supply logistic
- hybrid system
- neural network
- transport
- expert system
EN Many definitions of logistic management exist, but some of them are connected with the transport-warehouse chain, that includes obtaining raw materials, their processing, and selling. The environment, in which the company exists is also important. This is the reason why the optimization in a company should be made having in mind those aspects. The modification and optimization process in a company should include changes in its environment. The question is how long it takes for the company to adapt. Changes can be not significant in certain moment, but with the time they can have large influence on the company. It is not easy to recognize the size of the impact while consequences can be serious for the company. If the changes are recognized quickly, the company can conquer the difficulties by adaptation or can make changes in their processes. Sometimes the situation is not so simple, for example when the changes are detected too late or they require changes in management system. Improvement of the system can take a lot of time, i.e., year or more. This time would be shorter, if the system would change from itself, then the improvement of the system could be not so tedious and so consumptive of time. This is possible by implementation of elements of artificial intelligence in the system. Therefore the authors suggest a new concept of transport management, with use of an intelligent hybrid system, which adjusts to the current economic situation without updating or changing the management system and thanks to it the company can quickly replay with no outlay. The hybrid system consists of a neural network and three expert systems. It will influence the transport time by optimization of loading processes. In this manner the time and money will be spared. The loading surface and the number of trucks needed to transport the goods will be also optimized by continuous adaptation to the current economic situation.
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