A fractional-factorial probabilistic-possibilistic optimization framework for planning water resources management systems with multi-level parametric interactions

Abstract

In this study, a multi-level factorial-vertex fuzzy-stochastic programming (MFFP) approach is developed for optimization of water resources systems under probabilistic and possibilistic uncertainties. MFFP is capable of tackling fuzzy parameters at various combinations of α-cut levels, reflecting distinct attitudes of decision makers towards fuzzy parameters in the fuzzy discretization process based on the α-cut concept. The potential interactions among fuzzy parameters can be explored through a multi-level factorial analysis. A water resources management problem with fuzzy and random features is used to demonstrate the applicability of the proposed methodology. The results indicate that useful solutions can be obtained for the optimal allocation of water resources under fuzziness and randomness. They can help decision makers to identify desired water allocation schemes with maximized total net benefits. A variety of decision alternatives can also be generated under different scenarios of water management policies. The findings from the factorial experiment reveal the interactions among design factors (fuzzy parameters) and their curvature effects on the total net benefit, which are helpful in uncovering the valuable information hidden beneath the parameter interactions affecting system performance. A comparison between MFFP and the vertex method is also conducted to demonstrate the merits of the proposed methodology.