scholarly journals A Intelligent Logistics Inventory Distribution Model Based On Pipeline Network And Ant Colony Algorithm

2018 ◽  
Vol 53 ◽  
pp. 03046
Author(s):  
Jingwen Li ◽  
Yifei Tang ◽  
Jianwu Jiang
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Distribution Model ◽  
Efficient Operation ◽  
Logistics Industry ◽  
Pipeline Network ◽  
Distribution Station ◽  
Management Modes ◽  
Intelligent Logistics ◽  
The Individual

With the popularization and application of emerging Internet technologies such as big data and cloud computing, the traditional B2B and B2C warehousing logistics management modes have not achieved synergy between various distribution stations and suppliers, achieving “one-to-one” means a distribution station is supplied by a manufacturer, and a customer is also supplied by a distribution station. The traditional logistics industry model can no longer meet the individual needs of customers. At present, the logistics industry has a series of problems such as slow delivery, slow turnover, high cost and poor service. Based on the theoretical basis of pipeline network and smart logistics, this paper proposes a pipeline network model of intelligent logistics, and improves the ant colony algorithm to improve transportation efficiency, which provides a guarantee for the efficient operation of the intelligent logistics platform.

Parameter Estimation of Weibull Distribution Model Based on Ant Colony Algorithm

2014 ◽  
Vol 1070-1072 ◽  
pp. 2073-2078
Author(s):  
Xiu Ji ◽  
Hui Wang ◽  
Chuan Qi Zhao ◽  
Xu Ting Yan
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Weibull Distribution ◽  
Ant Colony Algorithm ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Ant Colony ◽  
Likelihood Method ◽  
Distribution Model ◽  
Algorithm Theory

It is difficult to estimate the parameters of Weibull distribution model using maximum likelihood estimation based on particle swarm optimization (PSO) theory for which is easy to fall into premature and needs more variables, ant colony algorithm theory was introduced into maximum likelihood method, and a parameter estimation method based on ant colony algorithm theory was proposed, an example was simulated to verify the feasibility and effectiveness of this method by comparing with ant colony algorithm and PSO.This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications. The best is to read these instructions and follow the outline of this text.

The Application of a Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem

2014 ◽  
Vol 556-562 ◽  
pp. 4693-4696
Author(s):  
Yue Li Li ◽  
Ai Hua Ren
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony System ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
Constrained Vehicle Routing ◽  
Capacity Constrained

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.

scholarly journals Intelligent Bionic Optimization Algorithm Based on the Growth Characteristics of Tree Branches

2021 ◽  
Vol 15 (2) ◽  
pp. 34-46
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Morphological Characteristics ◽  
Optimal Solution ◽  
Shoot Tip ◽  
Experimental Results ◽  
Ant Colony ◽  
The Individual

To improve the performance of bionic algorithms, an intelligent bionic optimization algorithm is proposed based on the morphological characteristics of trees growing toward light. The growth organ of the tree is mapped into the coding of the tree growth algorithm, and the entire tree is formed by selecting the fastest growing individual to form the next level of the tree. When the tree growth reaches a certain level, the individual code of the shoot tip is added to enhance the search ability of the individual shoot tip in the growth space of the entire tree. This method achieves a near-optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function. The experimental results show that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm or the ant colony algorithm.

Intelligent Learning Ant Colony Algorithm

2011 ◽  
Vol 48-49 ◽  
pp. 625-631 ◽  
Author(s):  
Jian Hua Ma ◽  
Fa Zhong Tian
Keyword(s):  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Effective Algorithm ◽  
Learning Mechanism ◽  
Numerical Examples ◽  
Improvement Method ◽  
Hard Problems ◽  
Concentration Degree ◽  
The Individual ◽  
Np Hard Problems

Ant colony algorithm is effective algorithm for NP-hard problems, but it also tends to mature early as other evolutionary algorithms. One improvement method of ant colony algorithm is studied in this paper. Intelligent learning ant colony algorithm with the pheromone difference and positive-negative learning mechanism is brought forward to solve TSP. The basic approach of ant colony algorithm is introduced firstly, then we introduced the individual pheromone matrix and positive-negative learning mechanism into ant colony algorithm. Next the steps of intelligent learning ant colony algorithm are given. At last the effectiveness of this algorithm is proved by random numerical examples and typical numerical examples. It is also proved that intelligent ant and learning mechanism will affect concentration degree of pheromone.

The Application of a Improved Hybrid Ant Colony Algorithm in Vehicle Routing Optimization Problem

2014 ◽  
Vol 556-562 ◽  
pp. 4005-4008
Author(s):  
Yue Li Li ◽  
Ai Hua Ren
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony System ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
Constrained Vehicle Routing ◽  
Capacity Constrained

With the development of the market economy, the logistics industry has been developed rapidly.It is easy to understand that good vehicle travel path planning has very important significance in the logistics company,especially in the general production enterprises. This paper mainly studies the microcosmic traffic system in the type of vehicle routing problems: capacity-constrained vehicle routing problem. We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem, treated as nodes in a spatial network. For the networks where the nodes are concentrated, the use of hybrid heuristic optimization can greatly improve the efficiency of the solution. The algorithm produces high-quality solutions for the capacity-constrained vehicle routing problem.

scholarly journals The Research on Low Carbon Logistics Routing Optimization Based on DNA-Ant Colony Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Liyi Zhang ◽  
Ying Wang ◽  
Teng Fei ◽  
Hongwei Ren
Keyword(s):  
Carbon Emissions ◽  
Optimization Model ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Ant Colony ◽  
Path Optimization ◽  
Low Carbon ◽  
Economic Activities ◽  
Logistics Industry ◽  
Routing Optimization

As the energy conservation and emission reduction and sustainable development have become the hot topics in the world, low carbon issues catch more and more attention. Logistics, which is one of the important economic activities, plays a crucial role in the low carbon development. Logistics leads to some significant issues about consuming energy and carbon emissions. Therefore, reducing energy consumption and carbon emissions has become the inevitable trend for logistics industry. Low carbon logistics is introduced in these situations. In this paper, from the microcosmic aspects, we will bring the low carbon idea in the path optimization issues and change the amount of carbon emissions into carbon emissions cost to establish the path optimization model based on the optimization objectives of the lowest cost of carbon emissions. According to different levels of air pollution, we will establish the double objectives path optimization model with the consideration of carbon emissions cost and economy cost. Use DNA-ant colony algorithm to optimize and simulate the model. The simulation indicates that DNA-ant colony algorithm could find a more reasonable solution for low carbon logistics path optimization problems.

scholarly journals Performance Analysis of Three Intelligent Algorithms on Route Selection of Fishbone Layout

2019 ◽  
Vol 11 (4) ◽  
pp. 1148 ◽  
Author(s):  
Li Zhou ◽  
Zhaochan Li ◽  
Ning Shi ◽  
Shaohua Liu ◽  
Ke Xiong
Keyword(s):  
Data Mining ◽  
Ant Colony Algorithm ◽  
Technological Development ◽  
Path Model ◽  
Practical Significance ◽  
Efficient Operation ◽  
Logistics Industry ◽  
The Sustainable Development ◽  
Multiple Orders ◽  
Global Economic Growth

The Internet of Things (IoT) has become an important strategy in the current round of global economic growth and technological development and provides a new path for the intelligent development of the logistics industry. With the development of the economy, the demand for logistics benefits is becoming more important. The appropriate use of technologies related to IoT to improve logistics efficiency, such as cloud computing, mobile computing and data mining, has become a topic of considerable research interest. Picking operations are currently an extremely important and cumbersome aspect of logistics center tasks. To shorten the picking distance and improve work efficiency, this paper uses the genetic algorithm, ant colony algorithm and cuckoo algorithm to optimize the picking path in a fishbone-layout warehouse and establishes an optimized model of the warehouse picking path under the fishbone layout. Data-mining technology is used to simulate the model and obtain the simulation data under the condition of multiple orders. The results provide a theoretical basis for the study of the fishbone-layout picking path model and has certain practical significance for the efficient operation of logistics enterprises. Through optimization, it is conducive to the sustainable development of enterprises and to achieving long-term profitability.

The improved intelligent bionic optimization algorithm based on the growth characteristics of tree branches

2021 ◽  
pp. 1-9
Author(s):  
Fei Tang
Keyword(s):  
Genetic Algorithm ◽  
Tree Growth ◽  
Optimization Algorithm ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Growth Characteristics ◽  
Ant Colony ◽  
Optimization Function ◽  
Growth Space ◽  
The Individual

To improve the optimization efficiency of the intelligent bionic optimization algorithm, this paper proposes intelligent bionic optimization algorithm based on the growth characteristics of tree branches. Firstly, the growth organ of the tree is mapped into the coding of the tree growth algorithm (intelligent bionic optimization algorithm). Secondly, the entire tree, that is the growing tree, is formed by selecting the individual that grows fast to generate the next level of shoot population. Lastly, if the growing tree reaches a certain level, the individual coding of the shoots is added to enhance the searching ability of the individuals of current generation in the growth tree growth space, so that the algorithm approaches the optimal solution. The experimental results were compared with the optimization results of the genetic algorithm and the ant colony algorithm using the classic optimization function and showed that this algorithm has fewer iterations, a faster convergence speed, higher precision, and a better optimization ability than the genetic algorithm and the ant colony algorithm.

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