Simulation of Agricultural Logistic Processes with k-Nearest Neighbors Algorithm

Abstract

The topic logistic has become more and more important in German agriculture during the last years. This is caused by a growth of enterprises and machines but also be the enormous extension of the cultivation of renewable resources for the production of energy.

To manage these logistical tasks in agriculture in Germany at the moment different transport systems are preferred. The classical system with tractor and agricultural trailer, transport via truck like it is typical for the commercial transport of goods and the transport with specialized vehicles which can be classified between both systems. To evaluate these transport processes it is decisive for the farmers to know the key parameters of the single systems like the average fuel consumption (energy) and the average transport speed (time) for their special logistic issue to optimize the input of resources. The aim of the examination is therefore to develop a planning tool for the farmers to evaluate the logistic systems.

Within this project examinations have shown that environmental influences like driver, loading, road type, gradient, winding ness, traffic situation, daytime and so on have an enormous influence on the key parameters energy and time.

Current simulation systems for the estimation of the key parameters energy and time have not provided satisfying results for the agricultural sector. Usually they are basing on linear and/or nonlinear equations with a very complex emphasis of the influencing factors and after technical changes on the logistic systems they cannot be used any longer. Furthermore always only a very small number of influencing factors can be integrated. Therefore in this examination the k-Nearest Neighbors algorithm (kNN) is used. This results in a much more flexible use of different influencing factors with a difference in weight. With the help of test data the system is learning and creating the kNN algorithm. This can be used for simulation. The advantage of this system of “artificial intelligence” is that the model building can be done in time in the area of the current working point. This makes it possible to integrate even unknown or in their effect not determinable environmental factors. By the training structure and the integration of new test data the algorithm is much more easily adaptable on new trends. There is also the possibility to train the system optimal on the own conditions with the help of own test data.

The k-Nearest Neighbours algorithm which has been determined during the examination makes it possible to estimate the key parameters energy and time for the logistic tasks in agriculture with a probability of more than 97%.