Abstract
In order to reveal the uncertainty in the process of rescuing the accidents over the expressway network, fuzzy programming method is used to establish the rescue resource dispatch model. The model aims to minimize the fuzzy dispatch decision-making time to reflect the accidents’ influence on the upper traffic flow after accidents happen on the fully-closed expressway. Fuzzy chance constraint is designed to reflect the relationship between the uncertain requirements of potential accidents and the existing resource allocation. According to the limitation of the traditional algorithm and the complexity of dispatch problems, especially many accidents happening simultaneously and various rescue resources required, the genetic algorithm based on fuzzy simulation is designed to be fit for the model and the optimized dispatch scheme is obtained. The case study of the expressway network in Henan Province is conducted to illustrate that the fuzzy dispatch method can be used to solve the conflicts between shortening the decision-making time and reducing the rescuing economic costs compared with the existing rescue mode, and the optimum rescue resource dispatch decision is made for the command control center.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chunlin, L., Zhaohan, S.: He Jianmin Selection of Multi-depot Based on the Continuous Consumption in Emergency Systems. Journal of Industrial Engineering 3, 13–16 (1999)
Jianmin, H., Chunlin, L., Haiyan, Y.: Selection of Multi-depot in Emergency Systems. Systems Engineering-theory and Practice 21, 89–93 (2001)
Shuping, G., Sanyang, L.: Scheduling Problem in Multi-resource Emergency Systems Based on the Connection Number. Systems Engineering-theory and Practice 23, 113–115 (2003)
Zografos, K.G., Androutsopoulos, K.N., Vasilakis, G.M.: A Real-time Decision Support System for Roadway Network Incident Response Logistics. Transportation Research Part C 6, 1–18 (2002)
Yingtao, C., Yuan, Z.: Dynamic vehicle scheduling in emergency management. Safety 6, 11–15 (2007)
Takeo, Y.: A network flow approach to a city emergency evacuation planning. International Journal of Systems Science 27, 931–936 (1996)
Sherali, H.D., Subramanian, S.: Opportunity cost-based models for traffic incident response problem. Journal of Transportation Engineering 125, 176–185 (1999)
Ying, S., Hong, C., Chuanliang, J.: Nonlinear Mixed-integer Programming Model for Emergency Resource Dispatching with Multi-path. Operations Research and Management Science 16, 5–8 (2007)
Pal, R., Bose, I.: An optimization based approach for deployment of roadway incident response vehicles with reliability constraints. European Journal of Operational Research 198, 452–463 (2009)
Canghui, Z., Qi, H., Gan, C.: Applicability Study of Traffic Rescue Resource Dispatch Method on Expressway. China Safety Science Journal 19, 165–171 (2009)
Baoding, L., Kakuzo, I.: Chance constrained programming with fuzzy parameters. Fuzzy Sets and Systems 94, 227–237 (1998)
Xiaoyu, Z., Dingwei, W.: Fuzzy Chance Constrained Programming Model for Bi-level Distribution Network Design in the Supply Chain. Control Theory and Applications 19, 249–252 (2002)
Baoding, L., Kakuzo, I.: Fuzzy programming with fuzzy decisions and fuzzy simulation-based genetic algorithm. Fuzzy Sets and Systems 122, 253–262 (2001)
Ling, W.: Intelligent Optimization Algorithms with Applications. Tsinghua University Publishing House, Beijing (2001)
Chipperfield, A., Fleming, P., Pohlheim, H., Fonseca, C.: Genetic algorithm toolbox manual. The University of Sheffield (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhu, C., Li, X., Chai, G. (2010). The Optimization Approach for Traffic Rescue Resource Dispatch on Expressway Based on Fuzzy Programming. In: Cao, By., Wang, Gj., Guo, Sz., Chen, Sl. (eds) Fuzzy Information and Engineering 2010. Advances in Intelligent and Soft Computing, vol 78. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14880-4_33
Download citation
DOI: https://doi.org/10.1007/978-3-642-14880-4_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14879-8
Online ISBN: 978-3-642-14880-4
eBook Packages: EngineeringEngineering (R0)