Study on Workshop Delivery Route Optimization Based on Ant Colony Algorithm

ICLEM 2010 ◽  
2010 ◽  
Author(s):  
Jinku Li ◽  
Jianliang Zhao
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Delivery 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.

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.

Pheromone-Based Ant Colony Algorithm for Optimal Proliferation of Research

2013 ◽  
Vol 734-737 ◽  
pp. 3152-3157
Author(s):  
Lei Lei Deng
Keyword(s):  
Optimization Design ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Climatic Conditions ◽  
Water Saving ◽  
Ant Colony ◽  
Path Model ◽  
Route Optimization ◽  
Path Optimization ◽  
Field Plots

In order to realize the water-saving irrigation of field plots path pipeline deployment management and control, the pheromone of ant colony algorithm for optimization design of. Ant colony algorithm (ACA) is a kind of Bionic Engineering with the development of the optimization algorithm, is mainly based on ant foraging in the search for the shortest path model and form. This article attempts in the existing ant colony algorithm combinatorial optimization of real defect on the basis of field plots, to coordinate as a data source, An improved ant colony algorithm for field plots wiring path design, thereby improving the ant colony algorithm in an iterative process to update the optimal solution ability, Finally in the same number of iterations to find path shorter, cost less rules, solve agricultural water-saving irrigation pipeline path optimization deployment problem. And in the VC++ program validation path optimization problems. The test results show that, under the same climatic conditions, route optimization design results can be deployed for water saving irrigation pipeline layout management provides reference basis and data support.

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