Optimization of warehouse management based on artificial intelligence technology

2021 ◽  
pp. 1-8
Author(s):  
Yang Li ◽  
Xianliang Shi ◽  
Hongdong Diao ◽  
Min Zhang ◽  
Yadong Wu
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Evaluation Index System ◽  
Warehouse Management ◽  
Problem Model ◽  
Warehouse Design

This paper analyzes the artificial intelligence algorithms related to the storage path optimization problem and focuses on the ant colony algorithm and genetic algorithm with better applicability. The genetic algorithm is used to optimize the parameters of the ant colony algorithm, and the performance of the ant colony algorithm is improved. A typical route optimization problem model is taken as an example to prove the effectiveness of parameter optimization. This paper proposes a combined forecasting method through data preprocessing algorithm and artificial intelligence optimization. The combined prediction method first uses wavelet transform threshold processing to remove the noise data in the original data and then uses three separate methods to reduce noise. Forecast warehouse data and obtain intermediate forecast results. This article analyzes warehouse management and can solve the problems in the company’s warehouse management from the aspects of warehouse design and planning, warehouse design, and integrated warehouse management. After comparative analysis and selection, this paper uses the SLP method to rationally adjust and arrange the relative position and area of each functional area of the warehouse, and improve the evaluation index system. Experimental research shows that under the guidance of this article to optimize storage strategy, cargo location layout, and warehousing workflow, the employee reward mechanism mobilizes the enthusiasm of employees, improves work efficiency, and reduces storage costs. The above-mentioned various optimization and storage improvement measures finally reduced the total storage cost by 17%, effectively achieving the goal of cost control.

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.

scholarly journals Wireless Sensor Network Route Optimization Based on Improved Ant Colony-Genetic

2015 ◽  
Vol 11 (9) ◽  
pp. 4 ◽  
Author(s):  
Wei Liu ◽  
Yongfeng Cui ◽  
Zhongyuan Zhao
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Optimization Problem ◽  
Ant Colony ◽  
Route Optimization ◽  
Wireless Sensor ◽  
Multi Objective Optimization ◽  
Mutation Operation

The objective of this paper is focuses on route optimization, for a given wireless sensor network. We detail the significance of route optimization problem and the corresponding mathematical model. After analyzing the complex multi-objective optimization problem, Ant Colony Optimization (ACO) algorithm was introduced to search the best route. Inspired by Genetic Algorithm (GA), we embed two operations into ACO to refine it. First, every ant after achieving sink will be regarded as an individual such as that in GA. The crossover operation will be applied and then, the generated new ants will replace the weaker parents. Second, we designed a mutation operation for ants selecting next nodes to visit. Experimental results demonstrate that the proposed combination algorithm has significant enhancements than both GA and ACO. The lifetime of WSN can be extended and the coverage speed can be accelerated.

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.

Optimization of Processing Parameters in Injection Molding Based on Adaptive Ant Colony Algorithm

2011 ◽  
Vol 179-180 ◽  
pp. 304-310 ◽  
Author(s):  
Feng Li Huang ◽  
Shui Sheng Chen ◽  
Jin Mei Gu
Keyword(s):  
Genetic Algorithm ◽  
Injection Molding ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Kriging Model ◽  
Ant Colony ◽  
Injection Molding Process ◽  
Adaptive Genetic Algorithm ◽  
Molding Process ◽  
Correlation Degree

An optimization method, integrating correlation degree, response surface method and ant colony algorithm, is proposed for exploring optimal parameters with the molding quality evaluation of warpage amount in injection molding. Initially, a novel formula calculating correlation degree is brought forth on the basis of the definition of distance and place value, and the parameters are chosen using the correlation degree method. Then the approximate model of the injection molding process is constructed by Kriging model with the determined parameters. Finally, the adaptive genetic algorithm and the ant colony algorithm are adopted to solve the optimization problem respectively, and injection molding tests are experimentally performed to validate the optimization results of parameters in injection molding. The experimental results demonstrate that the ant colony algorithm is superior to the genetic algorithm in solving the optimization problem for the low-dimensional design variables vector and the short coding length.

Partheno Genetic Ant Colony Optimization Algorithm and its Application

2010 ◽  
Vol 143-144 ◽  
pp. 1132-1136
Author(s):  
Guo Li Wang ◽  
Jian Hui Wu ◽  
Yu Su
Keyword(s):  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Initial Population ◽  
Ant Colony Optimization Algorithm ◽  
Genetic Character ◽  
Shortest Route ◽  
The Traveling Salesman Problem ◽  
Optimization Problem Solving

Ant colony algorithm is a kind of effective combinatorial optimization problem solving algorithm has been increasingly, thorough research, and gradually get used. Ant colony algorithm is a set of parameters, the algorithm, a lack of adequate experiences often. The paper has put forward a single genetic character of ant colony algorithm. Will the ant colony algorithm each search results as the initial population, single genetic improvement, for the shortest route optimization. In the traveling salesman problem of the experiments prove the effectiveness of the proposed algorithm.

Research on Vehicle Routing Optimization Based on Improved Ant Colony Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 1917-1920
Author(s):  
Rui Wang ◽  
Zai Tang Wang
Keyword(s):  
Vehicle Routing ◽  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Route Optimization ◽  
Local Optimum ◽  
Evaporation Coefficient ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Vehicle Routing Optimization

This paper analyzes the domestic and international logistics distribution route optimization problem and the research status of ant colony algorithm, illustrates the problems existing in the logistics distribution now. It reflects the necessity to research on the vehicle routing optimization problem. In order to increasing the ant colony algorithm’s convergence speed and avoiding to fall into local optimum, we improve the pheromone evaporation coefficient and visibility to optimize the searching ability, which can avoid premature convergence and stagnation.

scholarly journals Research on the Uncertainty Decision Model of the Regional Collaborative Innovation System Based on an Improved Ant Colony Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-10
Author(s):  
Xiaona Zhang ◽  
Fayin Wang
Keyword(s):  
Decision Model ◽  
Ant Colony Algorithm ◽  
Innovation System ◽  
Path Selection ◽  
Collaborative Optimization ◽  
Ant Colony ◽  
Evaluation Index System ◽  
Regional Innovation ◽  
Collaborative Innovation ◽  
Improved Ant Colony Algorithm

The regional collaborative innovation system is a nonlinear complex system, which has obvious uncertainty characteristics in the aspects of member selection and evolution. Ant colony algorithm, which can do the uncertainty collaborative optimization decision-making, is an effective tool to solve the uncertainty decision path selection problem. It can improve the cooperation efficiency of each subsystem and achieve the goal of effective cooperation. By analysing the collaborative evolution mechanisms of the regional innovation system, an evaluation index system for the regional collaborative innovation system is established considering the uncertainty of collaborative systems. The collaborative uncertainty decision model is constructed to determine the regional innovation system’s collaborative innovation effectiveness. The improved ant colony algorithm with the pheromone evaporation model is applied to traversal optimization to identify the optimal solution of the regional collaborative innovation system. The collaboration capabilities of the ant colony include pheromone diffusion so that local updates are more flexible and the result is more rational. Finally, the method is applied to the regional collaborative innovation system.

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