Publikace

informace pocházejí z univerzitní databáze V3S

Autoři:
Herrchen, P.; Kylakorpi, J.; Van Der Jeugt, T.; prof. Dr. Ing. Miroslav Svítek, dr. h. c.; doc. Ing. Tomáš Horák, Ph.D.; Ing. Václav Kožený, Ph.D., MBA
Publikováno:
2022, 2022 Smart City Symposium Prague (SCSP), Vienna, IEEE Industrial Electronic Society), ISBN 978-1-6654-7923-3, ISSN 2691-3666
Anotace:
This review paper gives a brief introduction to Smart City Logistics and uses different sources to ultimately present recommendations for Prague city to achieve better solutions in the area. With large numbers of people expected to move to cities in the coming thirty years, smart logistics will become even more important than it is today. Four different projects from three different cities in Europe are described, ITSLOG and SAILOR projects from Amsterdam, SmartPort from Hamburg and the Dachser project from Stuttgart. The benefits and learnings from these projects are presented with an additional SWOT-analysis which presents the opportunities and threats that could come with similar implementations in Prague. Recommendations based on the findings regarding smart loading zones, micro-hubs, and virtual depots and how they could be implemented in Prague are made. It turns out that their implementation could give several benefits for Prague and help the city become more smart, both in terms of logistics, but also in other ways.
DOI:
Typ:
Stať ve sborníku z prestižní konf.

Autoři:
Ing. Václav Kožený, Ph.D., MBA
Publikováno:
2015, Expert Systems with Applications, 42 (6), p. 2998-3004), ISSN 0957-4174
Anotace:
Credit scoring methods have been widely investigated by researchers; recently, genetic algorithms have attracted particular attention. Many research papers comparing the performance of genetic algorithms and traditional scoring techniques have been published, but most do not provide enough detail about the fitness function used by the genetic algorithm despite the fact that fitness function has a key influence on the model's overall performance. The aim of this paper is to evaluate the predictive performance of different fitness functions used by genetic algorithms in credit scoring. An alternative fitness function based on a variable bitmask is proposed, and its performance then compared with fitness functions based on a polynomial equation as well as an estimation of parameter range. The results suggest that the bitmask is superior to the two other methods in both accuracy and sensitivity. The Wilcoxon matched-pairs sign rank test and paired t-Test indicate these results are statistically significant. (C) 2014 Elsevier Ltd. All rights reserved.
DOI:
Typ:
Článek v periodiku excerpovaném SCI Expanded