Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment

A bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers...

Full description

Bibliographic Details
Main Authors: Jiuping Xu, Jun Gang, Xiao Lei
Format: Article
Language:English
Published: Hindawi Limited 2013-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2013/517372
id doaj-f21db1ad1d1e4c868de3008d9ada6167
record_format Article
spelling doaj-f21db1ad1d1e4c868de3008d9ada61672020-11-24T22:01:27ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472013-01-01201310.1155/2013/517372517372Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy EnvironmentJiuping Xu0Jun Gang1Xiao Lei2State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610064, ChinaUncertainty Decision-Making Laboratory, Sichuan University, Chengdu 610064, ChinaChina Three Gorges Corporation, Yichang 443002, ChinaA bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers first choose their routes so that the total transportation cost is minimized. Then, the emergency response department locates their emergency service units so as to maximize the total weighted arc length covered. In contrast to prior studies, the uncertainty associated with transportation risk has been explicitly considered in the objective function of our mathematical model. Specifically, our research uses a complex fuzzy variable to model transportation risk. An improved artificial bee colony algorithm with priority-based encoding is also applied to search for the optimal solution to the bilevel model. Finally, the efficiency of the proposed model and algorithm is evaluated using a practical case and various computing attributes.http://dx.doi.org/10.1155/2013/517372
collection DOAJ
language English
format Article
sources DOAJ
author Jiuping Xu
Jun Gang
Xiao Lei
spellingShingle Jiuping Xu
Jun Gang
Xiao Lei
Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
Mathematical Problems in Engineering
author_facet Jiuping Xu
Jun Gang
Xiao Lei
author_sort Jiuping Xu
title Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
title_short Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
title_full Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
title_fullStr Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
title_full_unstemmed Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment
title_sort hazmats transportation network design model with emergency response under complex fuzzy environment
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2013-01-01
description A bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers first choose their routes so that the total transportation cost is minimized. Then, the emergency response department locates their emergency service units so as to maximize the total weighted arc length covered. In contrast to prior studies, the uncertainty associated with transportation risk has been explicitly considered in the objective function of our mathematical model. Specifically, our research uses a complex fuzzy variable to model transportation risk. An improved artificial bee colony algorithm with priority-based encoding is also applied to search for the optimal solution to the bilevel model. Finally, the efficiency of the proposed model and algorithm is evaluated using a practical case and various computing attributes.
url http://dx.doi.org/10.1155/2013/517372
work_keys_str_mv AT jiupingxu hazmatstransportationnetworkdesignmodelwithemergencyresponseundercomplexfuzzyenvironment
AT jungang hazmatstransportationnetworkdesignmodelwithemergencyresponseundercomplexfuzzyenvironment
AT xiaolei hazmatstransportationnetworkdesignmodelwithemergencyresponseundercomplexfuzzyenvironment
_version_ 1725839394944843776