Application Research on Ant Colony Algorithm in Logistic Distribution Route-Optimization of Fresh Agricultural Products

2013 ◽  
Vol 7 (6) ◽  
pp. 391-399 ◽  
Author(s):  
RAN Wenxue ◽  
SHI Xinling ◽  
FU Huasen ◽  
YANG Guomin
Keyword(s):  
Ant Colony Algorithm ◽  
Agricultural Products ◽  
Ant Colony ◽  
Route Optimization ◽  
Logistic Distribution ◽  
Application Research

Application Research about the Chaos Ant Colony Algorithm in Emergency Logistics Distribution Routing Optimization

2011 ◽  
Vol 268-270 ◽  
pp. 1733-1738
Author(s):  
Teng Fei ◽  
Li Yi Zhang ◽  
Hong Wei Ren ◽  
Jin Zhang ◽  
Cui Wen Huang
Keyword(s):  
Quantitative Methods ◽  
Ant Colony Algorithm ◽  
Mathematic Model ◽  
Ant Colony ◽  
Experimental Simulation ◽  
Application Research ◽  
The Real ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Emergency Logistics

In this essay, the solution about emergency logistics distribution routing optimization has been analyzed by quantitative methods, and the mathematic model focusing on trying the best to shorten distribution time has been established, in which the actual situations of the pathways and the shortage of goods at each affected point have been considered in order to keep further close to the real circumstances where had suffered disaster. Using the Chaos Ant Colony Algorithm solves the mathematic model. The experimental simulation indicates that the arithmetic is feasible and effective to settle the problem about the optimization of emergency logistics distribution route.

scholarly journals Optimizing Route for Hazardous Materials Logistics Based on Hybrid Ant Colony Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Haixing Wang ◽  
Guiping Xiao ◽  
Zhen Wei
Keyword(s):  
Optimization Model ◽  
Network Flow ◽  
Road Network ◽  
Ant Colony Algorithm ◽  
Hazardous Materials ◽  
Construction Rule ◽  
Ant Colony ◽  
Route Optimization ◽  
Total Cost ◽  
Risk Limits

Optimizing Route for Hazardous Materials Logistics (ORHML) belongs to a class of problems referred to as NP-Hard, and a strict constraint of it makes it harder to solve. In order to dealing with ORHML, an improved hybrid ant colony algorithm (HACA) was devised. To achieve the purpose of balancing risk and cost for route based on the principle of ACA that used to solve TSP, the improved HACA was designed. Considering the capacity of road network and the maximum expected risk limits, a route optimization model to minimize the total cost is established based on network flow theory. Improvement on route construction rule and pheromone updating rule was adopted on the basis of the former algorithm. An example was analyzed to demonstrate the correctness of the application. It is proved that improved HACA is efficient and feasible in solving ORHML.

scholarly journals Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Zhixue Zhao ◽  
Xiamiao Li ◽  
Xiancheng Zhou
Keyword(s):  
Electric Vehicles ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Route Optimization ◽  
Cold Chain ◽  
Logistic Distribution ◽  
Time Varying ◽  
Traffic Conditions ◽  
Distribution Process ◽  
Fresh Products

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1761-1764
Author(s):  
Feng Kai Xu
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Low Cost ◽  
Ant Colony ◽  
Route Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Logistics Distribution ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

A Methodology and Modelling Technique for Taxi Path Optimization

2013 ◽  
Vol 711 ◽  
pp. 816-821
Author(s):  
Zhi Ping Hou ◽  
Feng Jin ◽  
Qin Jian Yuan ◽  
Yong Yi Li
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Routing Problem ◽  
Control Center ◽  
Material Resources ◽  
New Type

Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem. A new type of bionic algorithm-ant colony algorithm is very appropriate to solve Vehicle Routing Problem because of its positive feedback, robustness, parallel computing and collaboration features. In view of the taxi route optimization problem, this article raised the issue of the control of the taxi, by using the Geographic Information System (GIS), through the establishment of the SMS platform and reasonable taxi dispatch control center, combining ant colony algorithm to find the most nearest no-load taxi from the passenger, and giving the no-load taxi the best path to the passenger. Finally this paper use Ant Colony laboratory to give the simulation. By using this way of control, taxis can avoid the no-load problem effectively, so that the human and material resources can also achieve savings.

scholarly journals Simulation of Sports Venue Based on Ant Colony Algorithm and Artificial Intelligence

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Zhang ◽  
Weibo Sun ◽  
Sang-Bing Tsai
Keyword(s):  
Partial Order ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Order Relation ◽  
Ant Colony ◽  
Route Optimization ◽  
Sports Facilities ◽  
Routing Method ◽  
Evacuation Plan ◽  
Evacuation Route

In order to improve the congestion of the evacuation plan and further improve the evacuation efficiency, this paper proposes the priority Pareto partial order relation and the vector pheromone routing method based on the priority Pareto partial order relation. Numerical experiments show that compared with the hierarchical multiobjective evacuation path optimization algorithm based on the hierarchical network, the fragmented multiobjective evacuation path optimization algorithm proposed in this paper effectively improves the evacuation efficiency of the evacuation plan and the convergence of the noninferior plan set. However, the congestion condition of the noninferior evacuation plan obtained by the fragmented multiobjective evacuation route optimization algorithm is worse than the congestion condition of the noninferior evacuation plan obtained by the hierarchical multiobjective evacuation route optimization algorithm. The multiple factors that affect the routing process considered in the probability transfer function used in the traditional ant colony algorithm routing method must be independent of each other. However, in actual route selection, multiple factors that affect route selection are not necessarily independent of each other. In order to fully consider the various factors that affect the routing, this paper adopts the vector pheromone routing method based on the traditional Pareto partial order relationship instead of the traditional ant colony algorithm. The model mainly improves the original pheromone distribution and volatilization coefficient of the ant colony, speeds up the convergence speed and accuracy of the algorithm, and obtains ideal candidate solutions. The method is applied to the location of sports facilities and has achieved good results. The experimental results show that the improved ant colony algorithm model designed in this paper is suitable for solving the problem of urban sports facilities location in large-scale space.

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