The stock-flow model of spatial data infrastructure development refined by fuzzy logic

Abstract

The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average–Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

The coordinating contracts of supply chain in a fuzzy decision environment

“…Zhang et al ( 2014 ) adopted the crisp possibilistic mean method to study a two-level return contract in a fuzzy random demand environment. Recently, some researchers such as Boutkhoum et al ( 2015 ), Abdolmajidi et al ( 2016 ) and Hanine et al ( 2016 ) studied the applications of fuzzy theory in other optimal problems. Boutkhoum et al ( 2015 ) studied the industrial location selection problem in a fuzzy decision making environment.…”

“…Hanine et al ( 2016 ) proposed the fuzzy TODIM and fuzzy AHP methods to select the landfill location. Abdolmajidi et al ( 2016 ) used fuzzy logic for modeling the spatial data infrastructure development.…”