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The Scientific World Journal
Volume 2014, Article ID 542548, 15 pages
Research Article

An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

1School of Traffic & Transportation Engineering, Central South University, Changsha, Hunan 410075, China
2College of Transportation and Logistics, Central South University of Forestry and Technology, Changsha, Hunan 410004, China

Received 14 December 2013; Accepted 21 January 2014; Published 17 March 2014

Academic Editors: M. Caramia and F. R. B. Cruz

Copyright © 2014 Dezhi Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users’ demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators’ service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level.