Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Balázs Lénárt"'
Publikováno v:
Transportation Research Procedia. 27:301-308
Route planners are widely used nowadays. There are several available solutions that can support travelers planning their routes, however the number of combined route planners are low. Moreover, in most cases the customization possibilities are limite
Publikováno v:
Periodica Polytechnica Civil Engineering.
During travelling, more and more information must be taken into account, and travelers have to make several complex decisions. In order to support these decisions, IT solutions are unavoidable, and as the computational demand is constantly growing, t
Autor:
Balázs Lénárt, Krisztián Bóna
Publikováno v:
Periodica Polytechnica Transportation Engineering. 42:97-102
The demand planning is one of the most frequented topics in logistics planning within the supply chain management. The prediction of future demand is a very important phase in the enterprise resource planning in point of view of supporting other plan
Publikováno v:
MT-ITS
There are several algorithms which calculates shortest path, but most of them are not enough fast, effective and they provide only one solution. Therefore our investigation tries to find one alternative algorithm for this problem. In this paper it is
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783642289309
HAIS (2)
HAIS (2)
This paper presents an adaptive inventory control system based on neuro-fuzzy logic. In particular we describe a control system using adaptive neuro-fuzzy interference (ANFIS) for calculating the optimal value of the storage level of goods. An implem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::69618bef944328485160bec5f3db1746
https://doi.org/10.1007/978-3-642-28931-6_21
https://doi.org/10.1007/978-3-642-28931-6_21
Autor:
Balázs Lénárt
Publikováno v:
Periodica Polytechnica Transportation Engineering. 39:39
The paper investigates an artificial intelligence based demand forecasting method. A neural network driven automatic ARIMA model identification is being introduced. The limitations of the current methods are shown and a new identification concept is