A generalized fuzzy linear programming approach for environmental management problem under uncertainty

In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy...

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Vydáno v:Journal of the Air & Waste Management Association (1995) Ročník 62; číslo 1; s. 72 - 86
Hlavní autoři: Fan, Yurui, Huang, Guohe, Veawab, Amornvadee
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Taylor & Francis Group 01.01.2012
Taylor & Francis Ltd
Témata:
Air Pollution - economics
Air Pollution - prevention & control
Algorithms
Conservation of Natural Resources - methods
Emissions control
Environmental management
Environmental Monitoring - methods
Fuzzy
Fuzzy Logic
Fuzzy set theory
Linear programming
Mathematical models
Objective function
Policies
Power Plants
Software
Sulfur
Sulfur dioxide
Time Factors
Uncertainty
ISSN:1096-2247, 2162-2906
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Shrnutí:In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO 2 ) control planning model to identify effective SO 2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO 2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP, and then identify desired policies for SO 2 -emission control under uncertainty.
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ISSN:1096-2247
2162-2906
DOI:10.1080/10473289.2011.628901