scholarly journals Calibration of the SLEUTH urban simulation model using NOMAD and Genetic Algorithms

2021 ◽  
Vol 8 (1) ◽  
pp. 254-261
Author(s):  
André Koscianski ◽  
Leonardo Pedrozo Amaral
1974 ◽  
Vol 8 (1) ◽  
pp. 21-45 ◽  
Author(s):  
Eduardo E. Lozano ◽  
Michael Sena ◽  
Donald Heitzmann ◽  
Chih-H. Cheng

2018 ◽  
Vol 7 (10) ◽  
pp. 403 ◽  
Author(s):  
Yanlei Feng ◽  
Yi Qi

This paper introduces an urban growth simulation model applied to the full scope of China. The model uses a multicriteria decision analysis to calculate the land conversion probability and then integrates it with a cellular automata model. A nonlinear relationship is incorporated in to the model to interpret the impacts of different Land Use and Cover Change driving forces. The Analytical Hierarchical Process is also implemented to compute the variance between weights of different factors. Multiple sizes of neighborhood and different urban ratios in the model rules are tested, and a 5 × 5 neighborhood and an urban threshold of 0.33 are chosen. The study demonstrates the importance of spatial analysis on socioeconomic factors, population, and Gross Domestic Product in land use change simulation modeling. The model fills the gap between the purely economic theory simulation model and the geographic simulation model. The nationwide urban simulation is an example that addresses the lack of urban simulation studies in China and among large-scale simulation models.


2018 ◽  
Vol 35 (2) ◽  
pp. 1791-1806
Author(s):  
Orazio Giuffrè ◽  
Anna Granà ◽  
Maria Luisa Tumminello ◽  
Antonino Sferlazza

2011 ◽  
Vol 308-310 ◽  
pp. 914-917
Author(s):  
Ke Zhen Tang ◽  
Jun Fang Ni ◽  
Ke Gang Xu

The overall flow time of the coating workshop for cutting tools is set as the scheduling objective. The problems of production scheduling of coating workshop are described in detail, then the simulated modeling of the problems is constructed in the eM-Plant software, and the simulation model combines the genetic algorithms to solve the processing procedures. Compared with the simulated results at last, it is obvious that the production scheduling is greatly optimized and of excellent stability with genetic algorithms.


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