An inexact fuzzy-random-chance-constrained air quality management model

An inexact fuzzy-random-chance-constrained programming model (IFRCCMM) was developed for supporting regional air quality management under uncertainty. IFRCCMM was formulated through integrating interval linear programming within fuzzy-random-chance-constrained programming framework. It could deal wi...

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Vydáno v:Stochastic environmental research and risk assessment Ročník 27; číslo 8; s. 1929 - 1946
Hlavní autoři: Xu, Y., Shao, L. G., Huang, G. H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2013
Springer Nature B.V
Témata:
Air quality
Aquatic Pollution
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Decision making
Earth and Environmental Science
Earth Sciences
Environment
Fuzzy logic
Linear programming
Math. Appl. in Environmental Science
Original Paper
Outdoor air quality
Physics
Probability Theory and Stochastic Processes
Random number sampling
Random variables
Statistics for Engineering
Uncertainty
Waste Water Technology
Water Management
Water Pollution Control
Fuzzy-random-chance-constrained programming
Uncertainty
Interval linear programming
Air quality management
ISSN:1436-3240, 1436-3259
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Shrnutí:An inexact fuzzy-random-chance-constrained programming model (IFRCCMM) was developed for supporting regional air quality management under uncertainty. IFRCCMM was formulated through integrating interval linear programming within fuzzy-random-chance-constrained programming framework. It could deal with parameter uncertainties expressed as not only fuzzy random variables but also discrete intervals. Based on the stochastic and fuzzy chance-constrained programming algorithms, IFRCCMM was solved when constraints was satisfied under different satisfaction and violation levels of constraints, leading to interval solutions with different risk and cost implications. The proposed model was applied to a regional air quality management problem for demonstration. The obtained results indicated that the proposed model could effectively reflect uncertain components within air quality management system through employing multiple uncertainty-characterization techniques (in random, fuzzy and interval forms), and help decision makers analyze trade-offs between system economy and reliability. In fact, many types of solutions (i.e. conservative solutions with lower risks and optimistic solutions with higher risks) provided by IFRCCMM were suitable for local decision makers to make more applicable decision schemes according to their understanding and preference about the risk and economy. In addition, the modeling philosophy is general and applicable to many other environmental problems that may be complicated with multiple forms of uncertainties.
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ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-013-0728-8