| Logistics Park is the product of modem logistics development, and also the requirement of socioeconomic development and economy globalization. The formation and development of Logistics Park have its profound and inherent rules. To plan Logistics Park systematically and scientifically not only relates to the development of the logistics industries and other related industries, but the promotion of the regional economy. However, wastes of logistics resource and lower efficiency of logistics operations often occur in the construction of Logistics Park in our country due to scarcity of systematical and scientific planning schemes.Based on the existing literature on studying Logistics Park, the dissertation studies the development mechanism of Logistics Park and the optimization problems of spatial layout of the Logistics Park. The main contents are as follows.(1) The formation mechanism of Logistics Park. In this section, the background, internal and external effect factors, and impetus mechanism are studied. Also, the possible formation avenues and development rules of the Logistics Park are explored.(2) The evolvement rules of the Logistics Park. In this section, the correlation between logistics systems and other systems is analyzed in terms of the spatial-dynamic theory. The rules and conditions of evolvement of Logistics Park are investigated in terms of the dissipative structure theory. The findings show that in order to make the regional logistics systems evolve toward more efficient direction, it is very necessary to make a scientific and advanced policy for developing logistics industries. Moreover, it is also very necessary to improve the level of regional economy, optimize the structure of regional industry and enhance the construction of logistics infrastructure. Then, the space-time evolution rules of Logistics Park are analyzed and examined according to the cellular automata theory. Some feasible suggestions are proposed. Finally, a control models and solution algorithms for optimizing the regional logistics node synergism layout are presented. The basic ideas of the control model are as follows. The system takes the logistics nodes layout and logistics OD distribution as the input data of the system. Based on the analysis of system performance of the current logistics nodes and its ideal objectives of development, the output of the system gives the corresponding control strategies in terms of the development strategies and targets of the regional logistics system.(3) The optimization of spatial layouts of Logistics Park. In this section, we investigate the definition and intension of Logistics Park spatial layout optimization. Two key technical problems for the optimization of spatial layouts are pointed out. One is how to determine the optimal number, size, location as well as the rational matching relationship among them. The other is how to determine the optimal matching relationship between Logistics Park and the other types of logistics nodes. We also formulate the principles, methodologies and optimization approaches of the Logistics Park's layout.(4) The models and solution algorithms of spatial layout of the Logistics Park.Based on the analyses of existing studies on logistics node locations, the optimization problems of the spatial layouts of the Logistics Parks are studied in this section. Four models and the corresponding solution algorithms for static and dynamic problems are presented. The first is the location model of single Logistics Park in terms of grey incidence analysis and fuzzy evaluation theory. Based on the analyses of the impact factors on Logistics Park locating, the model combines the fuzzy appraisement and the gray incidence analyses to evaluate the candidate locations of Logistics Park. The second is the location model of multiple Logistics Parks with uncertain demand in terms of fuzzy programming methods. The model simultaneously considers the economics of scale on Logistics Park and the demand uncertainty of distribution centers. The model also provides a new method to convert the stochastic variables into fuzzy parameters. In the meantime, a hybrid genetic algorithm which combines the extended transportation problem is presented to solve the proposed model. The third is the rational service area and match relationship model of Logistics Park's cooperation with other logistics nodes, such as Logistics Center and Distribution Center. The fourth is the dynamic location model of multi-period and multiple logistics nodes. The optimization model considers such factors as the fixed cost of logistics nodes, handling cost of logistics nodes, expanding cost of logistics nodes, the economic of scale of logistics nodes, and the value of time of capitals. Then, an effective algorithm based on the heuristic principles and the hybrid genetic algorithm is developed to solve the proposed model. The first three models are static models, and the fourth is a dynamic model.
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