DocumentCode
507600
Title
Uncertainty Optimization Model for Emergency Resource Scheduling
Author
Li, Di ; Liu, GuangLi ; Gao, Youjian
Author_Institution
Coll. of Inf. & Electr. Eng., China Agric. Univ., Beijing, China
Volume
1
fYear
2009
fDate
Nov. 30 2009-Dec. 1 2009
Firstpage
55
Lastpage
58
Abstract
Emergency resource scheduling Optimization model with uncertainty is presented under the condition of disaster. Firstly, emergency resource scheduling problem is discussed in the transport path optimization. Then according to the actual situation of emergence decision-making, of chance-constrained model a model of fuzzy chance constrained programming (FCCP) based on the credibility theory is constructed with fuzzy times. And a hybrid intelligent algorithm for solving chance constrained programming models with fuzzy decisions was also designed. Due to the premature phenomenon of genetic algorithm (GA), the simulated annealing algorithm is introduced in which selection and mutation operator is modified,. So the convergent speed has been increased and premature phenomenon can be avoided. At last, data experiment verified the validity and rationality of the algorithm.
Keywords
constraint handling; decision making; disasters; emergency services; fuzzy set theory; scheduling; simulated annealing; chance-constrained model; credibility theory; disaster; emergence decision making; emergency resource scheduling; fuzzy chance constrained programming; fuzzy decisions; genetic algorithm; hybrid intelligent algorithm; mutation operator; selection operator; simulated annealing algorithm; transport path optimization; uncertainty optimization model; Chromium; Constraint theory; Decision making; Distribution functions; Educational institutions; Electronic mail; Joining processes; Knowledge acquisition; Transportation; Uncertainty; emergency resource scheduling; optimization model; uncertainty;
fLanguage
English
Publisher
ieee
Conference_Titel
Knowledge Acquisition and Modeling, 2009. KAM '09. Second International Symposium on
Conference_Location
Wuhan
Print_ISBN
978-0-7695-3888-4
Type
conf
DOI
10.1109/KAM.2009.313
Filename
5362209
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