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Wenlong C.,Moe Key Laboratory for Urban Transportation Complex Systems Theory | Huijun S.,Moe Key Laboratory for Urban Transportation Complex Systems Theory | Wang W.,Moe Key Laboratory for Urban Transportation Complex Systems Theory | Wu J.,Beijing Jiaotong University
Journal of Industrial Engineering and Management | Year: 2013

Purpose: The paper studies the price competition of a supply chain with one supplier and two competing retailers under occasional demand disruption. Design/methodology/approach: The supply chain is either decentralized or centralized. The demand disruption for two retailers occurs with different probability. We analyze the effect of occurrence probability of demand disruption on the optimal prices of the supplier and two retailers. Findings: We find that the profits of supplier, retailers and supply chain are decreasing with the occurrence probability of demand disruption. Originality/value: It is helpful for supply chain members to adjust the original contracts to demand disruption.


Sun H.-J.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Zhang H.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Wu J.-J.,Beijing Jiaotong University | Bi J.-T.,Chinese Academy of Sciences
Nonlinear Dynamics | Year: 2013

With the consideration of network structure and travelers' path choice behavior in the congested traffic network, we study the effects of network topologies with degree-degree uncorrelation and correlations (assortative and disassortative correlation) on the performance of traffic network. How the effects of rewiring probability and the degree distribution exponent impact on the traffic congestion and the network efficiency are mainly analyzed. It is found that the correlated network is more vulnerable to traffic congestion compared with uncorrelated ones. Therefore, the uncorrelated network can support much more volume of traffic and have a larger carrying capacity when the traveler's choice behavior is considered. © 2013 Springer Science+Business Media Dordrecht.


Sun H.-J.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Zhang H.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Wu J.-J.,Beijing Jiaotong University
Nonlinear Dynamics | Year: 2012

Many transport processes on network depend crucially on the underlying network topology. In this paper, we propose a model to generate correlated scale free transportation networks with community structure by considering the mechanisms of dynamical network evolution and rewiring links. With the introduction of congestion effects, we investigate the performance and carrying capacity of this network. The results show that congestion in the uncorrelated network is more serious than the assortative or disassortative ones. Therefore, the correlated network with communities can bear much more traffic flow. In addition, the networks with lager modularity can enhance the transportation efficiently. © Springer Science+Business Media B.V. 2012.


Sun H.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Wu J.,Beijing Jiaotong University | Wang W.,MOE Key Laboratory for Urban Transportation Complex Systems Theory | Gao Z.,MOE Key Laboratory for Urban Transportation Complex Systems Theory
Information Sciences | Year: 2014

As the demands on urban transportation networks grow rapidly, problems of network design have attracted a great deal of interest because of the need to effectively handle urban transport planning using information technology. A bi-level continuous network design model is proposed in this paper to address the optimal road capacity expansion of existing links. Based on the fact that every origin-destination demand is random and affected by traffic travel information, the network is subject to relatively minimal day-to-day events of stochastic link capacity variations. Therefore, the primary objective is to maximize the reliability of the total travel time, while the lower level model, utilizing the behaviors of stochastic route choice, is aimed at reducing drivers' travel time uncertainty through traffic information provided by advanced traveler information systems. The Particle Swarm Optimization algorithm is used to solve the suggested model, and a numerical example using the Sioux Falls network is provided. The computation results show that travel time reliability is improved by system optimization using traffic information. © 2014 Published by Elsevier Inc.

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