scholarly journals Multiperiod Hierarchical Location Problem of Transit Hub in Urban Agglomeration Area

2017 ◽  
Vol 2017 ◽  
pp. 1-15
Author(s):  
Ting-ting Li ◽  
Rui Song ◽  
Shi-wei He ◽  
Ming-kai Bi ◽  
Wei-chuan Yin ◽  
...  
Keyword(s):  
Travel Time ◽  
Urban Population ◽  
Location Problem ◽  
Clonal Selection ◽  
Urban Agglomeration ◽  
Population Migration ◽  
Clonal Selection Algorithm ◽  
Rapid Urbanization ◽  
Transportation Demand ◽  
Hierarchical Location

With the rapid urbanization in developing countries, urban agglomeration area (UAA) forms. Also, transportation demand in UAA grows rapidly and presents hierarchical feature. Therefore, it is imperative to develop models for transit hubs to guide the development of UAA and better meet the time-varying and hierarchical transportation demand. In this paper, the multiperiod hierarchical location problem of transit hub in urban agglomeration area (THUAA) is studied. A hierarchical service network of THUAA with a multiflow, nested, and noncoherent structure is described. Then a multiperiod hierarchical mathematical programming model is proposed, aiming at minimizing the total demand weighted travel time. Moreover, an improved adaptive clonal selection algorithm is presented to solve the model. Both the model and algorithm are verified by the application to a real-life problem of Beijing-Tianjin-Hebei Region in China. The results of different scenarios in the case show that urban population migration has a great impact on the THUAA location scheme. Sustained and appropriate urban population migration helps to reduce travel time for urban residents.

scholarly journals Robust Optimization Model and Algorithm for Railway Freight Center Location Problem in Uncertain Environment

2014 ◽  
Vol 2014 ◽  
pp. 1-6
Author(s):  
Xing-cai Liu ◽  
Shi-wei He ◽  
Rui Song ◽  
Yang Sun ◽  
Hao-dong Li
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Location Problem ◽  
Clonal Selection ◽  
Expected Value ◽  
Clonal Selection Algorithm ◽  
Selection Algorithm ◽  
Railway Freight ◽  
Center Location ◽  
Robust Model

Railway freight center location problem is an important issue in railway freight transport programming. This paper focuses on the railway freight center location problem in uncertain environment. Seeing that the expected value model ignores the negative influence of disadvantageous scenarios, a robust optimization model was proposed. The robust optimization model takes expected cost and deviation value of the scenarios as the objective. A cloud adaptive clonal selection algorithm (C-ACSA) was presented. It combines adaptive clonal selection algorithm with Cloud Model which can improve the convergence rate. Design of the code and progress of the algorithm were proposed. Result of the example demonstrates the model and algorithm are effective. Compared with the expected value cases, the amount of disadvantageous scenarios in robust model reduces from 163 to 21, which prove the result of robust model is more reliable.

scholarly journals The Construction and Optimization of Ecological Security Pattern in the Harbin-Changchun Urban Agglomeration, China

2019 ◽  
Vol 16 (7) ◽  
pp. 1190 ◽  
Author(s):  
Rong Guo ◽  
Tong Wu ◽  
Mengran Liu ◽  
Mengshi Huang ◽  
Luigi Stendardo ◽  
...  
Keyword(s):  
Urban Agglomeration ◽  
Ecosystem Functions ◽  
Ecological Security ◽  
Least Cost Path ◽  
Rapid Urbanization ◽  
Songhua River ◽  
Urban Agglomerations ◽  
Ecological Corridors ◽  
Ecological Protection ◽  
Security Pattern

Urban agglomerations have become a new geographical unit in China, breaking the administrative fortresses between cities, which means that the population and economic activities between cities will become more intensive in the future. Constructing and optimizing the ecological security pattern of urban agglomerations is important for promoting harmonious social-economic development and ecological protection. Using the Harbin-Changchun urban agglomeration as a case study, we have identified ecological sources based on the evaluation of ecosystem functions. Based on the resistance surface modified by nighttime light (NTL) data, the potential ecological corridors were identified using the least-cost path method, and key ecological corridors were extracted using the gravity model. By combining 15 ecological sources, 119 corridors, 3 buffer zones, and 77 ecological nodes, the ecological security pattern (ESP) was constructed. The main land-use types composed of ecological sources and corridors are forest land, cultivated land, grassland, and water areas. Some ecological sources are occupied by construction, while unused land has the potential for ecological development. The ecological corridors in the central region are distributed circularly and extend to southeast side in the form of tree branches with the Songhua River as the central axis. Finally, this study proposes an optimizing pattern with "four belts, four zones, one axis, nine corridors, ten clusters and multi-centers" to provide decision makers with spatial strategies with respect to the conflicts between urban development and ecological protection during rapid urbanization.

scholarly journals Optimization on Vehicle Routing Problem Based on Immune Clonal Selection Algorithm

2012 ◽  
Vol 6-7 ◽  
pp. 256-260
Author(s):  
Hai Hua Li ◽  
Zong Yan Xu ◽  
Fei Fei Zhou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clonal Selection ◽  
Solution Space ◽  
Genetic Mutation ◽  
Initial Population ◽  
Local Optimum ◽  
Clonal Selection Algorithm ◽  
Routing Problem ◽  
Optimum Solution

Vehicle routing problem is a typical NP-hard problem and is difficult to get an optimum solution. Aiming at the shortages of the existing methods, this paper proposed an algorithm based on immune clonal selection to solve vehicle routing problem. In the algorithm, expressed antibody with matrix, generated the initial population of antibodies randomly, and employed the operations such as clonal selection, genetic mutation iteratively to search optimum solution in solution space. The experimental results show that the algorithm presented here can converge to the global optimum solution rapidly, overcoming such disadvantages of the genetic algorithm as slower convergent velocity and the convergence to a local optimum solution.

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