Systems dynamics modelling, simulation and optimization of integrated urban systems: a soft computing approach

Abstract

A systems dynamics (SD) simulation model has been developed to analyse dynamics of system behaviour in terms of various performance indicators representing city problems, on one hand, and city development, on the other, with three types of policy interventions: changes in the level of sectoral activities, structural changes in different sectors; and changes in the tolerable city problems index. An artificial neurals network (ANN) model has been successfully trained and used as a quick response model for fast feature extraction of the dynamics of the integrated urban energy‐economy‐environment system such that the outputs are within reasonable acceptable error for values of inputs covered by the input space of training patterns. For the sake of further convenience and effectiveness in policy decision making, optimised simulation trajectories are generated by applying genetic algorithms (GAs) search and optimisation methods for alternative policy scenarios of input variables. An application is shown in the context of the city of Jaipur.

Citation

Satsangi, P.S., Mishra, D.S., Gaur, S.K., Singh, B.K. and Jain, D.K. (2003), "Systems dynamics modelling, simulation and optimization of integrated urban systems: a soft computing approach", Kybernetes, Vol. 32 No. 5/6, pp. 808-817. https://doi.org/10.1108/03684920210443879