Robust stochastic fuzzy possibilistic programming for environmental decision making under uncertainty.

摘要: Abstract Nonpoint source (NPS) water pollution is one of serious environmental issues, especially within an agricultural system. This study aims to propose a robust chance-constrained fuzzy possibilistic programming (RCFPP) model for quality management system, where solutions farming area, manure/fertilizer application amount, and livestock husbandry size under different scenarios are obtained interpreted. Through improving upon the existing programming, approaches, RCFPP can effectively reflect complex system features uncertainty, implications quality/quantity restrictions achieving regional economic development objectives studied. By delimiting uncertain decision space through dimensional enlargement original constraints, enhances robustness optimization processes resulting solutions. The results case indicate that useful information be proposed providing feasible schemes activities (combinations p-necessity pi levels). A level represents certainty or necessity degree imprecise objective function, while means probabilities at which constraints will violated. desire acquire high income would decrease event maximization satisfied, potentially violate standards; willingness accept low run into risk potential failure. variables combined levels were makers justify and/or adjust incorporation their implicit knowledge. also suggest this developed approach applicable many practical problems probabilistic distribution simultaneously exist.