Interaction Steady State Transportation Network and Planning Optimization Model

2019 ◽  
pp. 771-780
Author(s):  
Baiyu Chen ◽  
Da Fu ◽  
Yuanyuan Yang
Keyword(s):  
Steady State ◽  
Optimization Model ◽  
Transportation Network ◽  
Planning Optimization

scholarly journals Optimizing Transportation Network of Recovering End-of-Life Vehicles by Compromising Program in Polymorphic Uncertain Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-24 ◽  
Author(s):  
Jing Zhang ◽  
Jingjing Liu ◽  
Zhong Wan
Keyword(s):  
End Of Life ◽  
Optimization Model ◽  
Transportation Network ◽  
Rapid Development ◽  
Production Capacity ◽  
Secondary Market ◽  
Fixed Cost ◽  
Selling Price ◽  
Uncertain Environment ◽  
Interactive Algorithm

With rapid development of technology and improvement of living standards, the per capita holding of automobiles greatly increases, and the amount of end-of-life vehicles (ELVs) becomes larger and larger such that it is valuable to investigate an effective strategy for recycling ELVs from the viewpoints of environmental protection and resource utilization. In this paper, an optimization model with fuzzy and stochastic parameters is built to formulate the transportation planning problems of recycling ELVs in polymorphic uncertain environment, where the unit processing and transportation costs, the selling price of reused items, and the fixed cost are all fuzzy, while the demand in secondary market and the production capacity are random owing to features underlying the practical data. For this complicated polymorphic uncertain optimization model, a unified compromising approach is proposed to hedge the uncertainty of this model such that some powerful optimization algorithms can be applied to make an optimal recycling plan. Then, an interactive algorithm is developed to find a compromising solution of the uncertain model. Numerical results show efficiency of the algorithm and a number of important managerial insights are revealed from the proposed model by scenario analysis and sensitivity analysis.

Logistics Network Optimization of Import Crude Oil in China Based on Genetic Algorithm

2014 ◽  
Vol 945-949 ◽  
pp. 3126-3129 ◽  
Author(s):  
Jie Chen
Keyword(s):  
Genetic Algorithm ◽  
Crude Oil ◽  
Objective Function ◽  
Network Optimization ◽  
Optimization Model ◽  
Transportation Network ◽  
Original Data ◽  
Coastal Areas ◽  
Logistics Network ◽  
Oil Transportation

The primary goal of this paper is to save logistics cost and reach optimizing configuration of import crude oil transportation network. An optimization model is put forward with an objective function of minimum logistics expense. It is carried out by Genetic Algorithm (GA) and MATLAB with original data of 2012 and predicted data of 2017. Results indicate that large VLCC of 260000-320000 tons is the main tanker type in import crude oil transportation network. And crude oil logistics bases will be formed which are represented by Qingdao, Dalian, Tianjin, Ningbo-Zhoushan, Zhanjiang and Huizhou in coastal areas.

scholarly journals Construction of the Optimization Model of Cargo Transport Network

2021 ◽  
Vol 261 ◽  
pp. 03013
Author(s):  
Yanyan Kong ◽  
Benfang Tian ◽  
Qingyang Wang ◽  
Donghui Liu ◽  
Yunfei Gao ◽  
...  
Keyword(s):  
Optimization Model ◽  
High Efficiency ◽  
Transportation Network ◽  
Transportation Cost ◽  
Circulation System ◽  
Time Cost ◽  
Transportation Modes ◽  
Consumption Cost ◽  
The Cost ◽  
In Transit

The transportation is gradually integrated into the circulation system of bulk goods, and developing and growing. Transportation has the advantages of high efficiency, high quality and green environment, In time, efficiency and cost than the traditional bulk transport has certain advantages. This paper analyzes the cost of logistics transportation network, which mainly includes transportation cost in transit, transit cost, time cost of cargo transportation and special cost. This paper discusses in detail the transportation cost, transit cost, time cost and carbon consumption cost of different transportation modes in the process of “scattered transformation”, and constructs the optimization model of” scattered transformation “transportation network with the least comprehensive transportation cost including the above costs.

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