The conflict among environmental and economic goals is a constant problem that always bothers the decision makers in regional planning. Besides, the uncertainties regarding the fuzzy goals in decision making and the i...
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The conflict among environmental and economic goals is a constant problem that always bothers the decision makers in regional planning. Besides, the uncertainties regarding the fuzzy goals in decision making and the impreciseness of parameter values in a system always create additional difficulties in management planning and analysis. This paper develops a new approach, a grey fuzzy multiobjective linear programming (GFMOLP) method, for the evaluation of sustainable management strategies for systems analysis. In particular, it demonstrates how uncertain messages of the planning objectives can be quantified by specific membership functions and combined through the use of grey numbers in a multiobjective analytical framework. It is verified that such an approach can successfully reflect system complexity and generate more flexible and robust management policies. Copyright (C) 1996 Elsevier Science Ltd
We first survey the application of fuzzy sets theory to various problems occurring in waterresourcessystems. The problem of optimal flood control planning by an appropriate integration of structural and non-structur...
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We first survey the application of fuzzy sets theory to various problems occurring in waterresourcessystems. The problem of optimal flood control planning by an appropriate integration of structural and non-structural measures with the objective of optimizing the flood damage reduction due to recurrent floods is modeled via fuzzy sets methodologies. A two level optimization model appropriate for planning decisions first on a regional or local level and then on a national level is proposed. A third phase involving coordination is appended. In particular, a fuzzy optimization model involving an adroit combination of fuzzy dynamic programming-type reasoning and a branch and bound-type search procedure is offered as a more utilitarian approach than its crisp equivalent. The performance of the algorithm is illustrated with an example.
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