scholarly journals Synthetic Optimization Model and Algorithm for Railway Freight Center Station Location and Wagon Flow Organization Problem

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xing-cai Liu ◽  
Shi-wei He ◽  
Rui Song ◽  
Hao-dong Li ◽  
Long Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Solution Method ◽  
Clonal Selection ◽  
Clonal Selection Algorithm ◽  
Railway Transport ◽  
Rail Network ◽  
Railway Freight ◽  
Organization Problem ◽  
Station Location ◽  
Operation Cost

The railway freight center stations location and wagon flow organization in railway transport are interconnected, and each of them is complicated in a large-scale rail network. In this paper, a two-stage method is proposed to optimize railway freight center stations location and wagon flow organization together. The location model is present with the objective to minimize the operation cost and fixed construction cost. Then, the second model of wagon flow organization is proposed to decide the optimal train service between different freight center stations. The location of the stations is the output of the first model. A heuristic algorithm that combined tabu search (TS) with adaptive clonal selection algorithm (ACSA) is proposed to solve those two models. The numerical results show the proposed solution method is effective.

Efficient Power Control and Clustering Method Based on Hybrid Clonal Genetic Algorithm in Wireless Sensor Networks

2012 ◽  
Vol 263-266 ◽  
pp. 889-897
Author(s):  
Xiang Xian Zhu ◽  
Su Feng Lu
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Large Scale ◽  
Clonal Selection ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Clonal Selection Algorithm ◽  
Clustering Methods

Wireless sensor networks (WSNs) lifetime for large-scale surveillance systems is defined as the time span that all targets can be covered. How to manage the combination of the sensor nodes efficiently to prolong the whole network’s lifetime while insuring the network reliability, it is one of the most important problems to research in WSNs. An effective optimization framework is then proposed, where genetic algorithm and clonal selection algorithm are hybridized to enhance the searching ability. Our goal can be described as minimizing the number of active nodes and the scheduling cost, thus reducing the overall energy consumption to prolong the whole network’s lifetime with certain coverage rate insured. We compare the proposed algorithm with different clustering methods used in the WSNs. The simulation results show that the proposed algorithm has higher efficiency and can achieve better network lifetime and data delivery at the base station.

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 Improvement of the Last Mile-Specific Issues in Railway Freight Transport

2020 ◽  
Vol 12 (23) ◽  
pp. 10154
Author(s):  
Adrián Šperka ◽  
Martin Vojtek ◽  
Jaromír Široký ◽  
Juraj Čamaj
Keyword(s):  
Customer Orientation ◽  
Freight Transport ◽  
Point Of View ◽  
Railway Transport ◽  
Ecological Process ◽  
Place Of Delivery ◽  
Rail Network ◽  
Railway Freight ◽  
Last Mile ◽  
Negative Impacts

The last mile is characterized as the last step of delivery to the customer from the logistics point of view. It is necessary to aim and fulfill all customers’ needs mainly during the process of the last mile, because it is directly connected to them. Customer orientation can cause many complications that must be solved according to their will. This part of the supply chain is currently under pressure. Nowadays, trends have changed the last mile into a more ecological process mostly in the transport field. Railway transport is considered as an ecological transport mode therefore the last mile should be done by the railway transport. Preconditions for doing the last mile by the railway transport is the existence of siding (special purpose tracks) at the place of delivery. Our research focuses on minimizing the negative impacts of the last mile to customers in the context of using sidings. This research is based on the real conditions of the Slovak rail network, and on consultation with some experts from freight transport companies.

Reactive Power Optimization Based on Immune Clonal Selection Algorithm

2014 ◽  
Vol 1030-1032 ◽  
pp. 1751-1754
Author(s):  
Zheng Yuan Li ◽  
Gang Zhang ◽  
Chao Li ◽  
Wei Zheng ◽  
Jing Jing Zheng
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Genetic Manipulation ◽  
Clonal Selection ◽  
Current Status ◽  
Clonal Selection Algorithm ◽  
Reactive Power Optimization ◽  
Selection Algorithm

Based on the full understanding of the current status of the reactive power optimization study, we propose an improved type of immune algorithm to solve the reactive power optimization problem by introducing the immune clonal selection algorithm (ICSA) genetic manipulation, affinity of mature, cloning and memory mechanism, and use the appropriate operator to ensure that the algorithm can quickly converge to the global optimal solution to improve the efficiency of the algorithm solving and solution accuracy, avoiding the "curse of dimensionality" and precocious problems. ICSA algorithm is proposed to improve the convergence speed simultaneously. Better maintain the diversity of the population. Effectively overcome the premature convergence of evolutionary computation itself is difficult to solve the problem. Four different examples of calculation results show that this method has superior computational efficiency and convergence capability, high quality and are solved, very suitable for solving large-scale power system reactive power optimization problem, with a strong practical value.

scholarly journals Dynamic Planning of Bicycle Stations in Dockless Public Bicycle-sharing System Using Gated Graph Neural Network

2021 ◽  
Vol 12 (2) ◽  
pp. 1-22
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Keqin Li ◽  
Philip S. Yu ◽  
Zeng Zeng
Keyword(s):  
Large Scale ◽  
Sequence Data ◽  
Location Prediction ◽  
City Management ◽  
Dynamic Planning ◽  
Graph Sequence ◽  
Graph Modeling ◽  
The Government ◽  
Location Clustering ◽  
Station Location

Benefiting from convenient cycling and flexible parking locations, the Dockless Public Bicycle-sharing (DL-PBS) network becomes increasingly popular in many countries. However, redundant and low-utility stations waste public urban space and maintenance costs of DL-PBS vendors. In this article, we propose a Bicycle Station Dynamic Planning (BSDP) system to dynamically provide the optimal bicycle station layout for the DL-PBS network. The BSDP system contains four modules: bicycle drop-off location clustering, bicycle-station graph modeling, bicycle-station location prediction, and bicycle-station layout recommendation. In the bicycle drop-off location clustering module, candidate bicycle stations are clustered from each spatio-temporal subset of the large-scale cycling trajectory records. In the bicycle-station graph modeling module, a weighted digraph model is built based on the clustering results and inferior stations with low station revenue and utility are filtered. Then, graph models across time periods are combined to create a graph sequence model. In the bicycle-station location prediction module, the GGNN model is used to train the graph sequence data and dynamically predict bicycle stations in the next period. In the bicycle-station layout recommendation module, the predicted bicycle stations are fine-tuned according to the government urban management plan, which ensures that the recommended station layout is conducive to city management, vendor revenue, and user convenience. Experiments on actual DL-PBS networks verify the effectiveness, accuracy, and feasibility of the proposed BSDP system.

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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