scholarly journals Solution to Solid Wood Board Cutting Stock Problem

2021 ◽  
Vol 11 (17) ◽  
pp. 7790
Author(s):  
Min Tang ◽  
Ying Liu ◽  
Fenglong Ding ◽  
Zhengguang Wang
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Solid Wood ◽  
Economic Benefits ◽  
Cutting Stock Problem ◽  
Ant Colony ◽  
Cutting Stock ◽  
Utilization Rate ◽  
Immune Genetic Algorithm ◽  
Wood Board

In the production process for wooden furniture, the raw material costs account for more than 50% of furniture costs, and the utilization rate of raw materials depends mainly on the layout scheme. Therefore, a reasonable layout is an important measure to reduce furniture costs. This paper investigates the solid wood board cutting stock problem (CSP) and establishes an optimization model, with the goal of the highest possible utilization rate for original boards. An ant colony-immune genetic algorithm (AC-IGA) is designed to solve this model. The solutions of the ant colony algorithm are used as the initial population of the immune genetic algorithm, and the optimal solution is obtained using the immune genetic algorithm after multiple iterations are transformed into the accumulation of global pheromones, which improves the search ability and ensures the solution quality. The layout process of the solid wood board is abstracted into the construction process of the solution. At the same time, in order to prevent premature convergence, several improved methods, such as a global pheromone hybrid update and adaptive crossover probability, are proposed. Comparative experiments are designed to verify the feasibility and effectiveness of the AC-IGA, and the experimental results show that the AC-IGA has better solution precision and global search ability compared with the ant colony algorithm (ACA), genetic algorithm (GA), grey wolf optimizer (GWO), and polar bear optimization (PBO). The utilization rate increased by more than 2.308%, which provides effective theoretical and methodological support for furniture enterprises to improve economic benefits.

scholarly journals Defect Removal and Rearrangement of Wood Board Based on Genetic Algorithm

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Yutu Yang ◽  
Zilong Zhuang ◽  
Yabin Yu
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Great Influence ◽  
Optimal Solution ◽  
Solid Wood ◽  
Raw Material ◽  
Machining Process ◽  
Utilization Rate ◽  
Standard Size ◽  
Wood Board

Defects on a solid wood board have a great influence on the aesthetics and mechanical properties of the board. After removing the defects, the board is no longer the standard size; manual drawing lines and cutting procedure is time-consuming and laborious; and an optimal solution is not necessarily obtained. Intelligent cutting of the board can be realized using a genetic algorithm. However, the global optimal solution of the whole machining process cannot be obtained by separately considering the sawing and splicing of raw materials. The integrated consideration of wood board cutting and board splicing can improve the utilization rate of the solid wood board. The effective utilization rate of the board with isolated consideration of raw material sawing with standardized dimensions of wood pieces and board splicing is 79.1%, while the shortcut splicing optimization with non-standardized dimensions for the final board has a utilization rate of 88.6% (which improves the utilization rate by 9.5%). In large-scale planning, the use of shortcut splicing optimization also increased the utilization rate by 12.14%. This has certain guiding significance for actual production.

scholarly journals Optimization of one-dimensional wire cutting with variable length based on genetic ant colony algorithm

2021 ◽  
Vol 336 ◽  
pp. 02011
Author(s):  
Xin Xia ◽  
Defu Wan
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Raw Materials ◽  
Optimal Solution ◽  
Ant Colony ◽  
Utilization Rate ◽  
Fusion Algorithm ◽  
One Dimensional ◽  
Experimental Parameters ◽  
Wire Cutting

This paper presents an algorithm of one-dimensional wire cutting based on genetic algorithm and ant colony algorithm. Firstly, the dominant solution is screened out by genetic algorithm and transformed into the initial accumulation of pheromone in ant colony algorithm, and then the ant colony algorithm is used to adjust the dominant solution of genetic algorithm to obtain the approximate optimal solution. The experimental results show that the convergence rate of the fusion algorithm is faster than that of the ant colony algorithm, and the utilization rate of raw materials is higher than that of genetic algorithm. In addition, the optimal parameters are obtained by adjusting the experimental parameters of the fusion algorithm.

An Improved Ant Colony Optimization Algorithm for Solving the TSP Problem

2010 ◽  
Vol 26-28 ◽  
pp. 620-624 ◽  
Author(s):  
Zhan Wei Du ◽  
Yong Jian Yang ◽  
Yong Xiong Sun ◽  
Chi Jun Zhang ◽  
Tuan Liang Li
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Convergence Time ◽  
Ant Colony Optimization Algorithm ◽  
Data Set ◽  
Research Attention ◽  
Simply Supported ◽  
Source Data

This paper presents a modified Ant Colony Algorithm(ACA) called route-update ant colony algorithm(RUACA). The research attention is focused on improving the computational efficiency in the TSP problem. A new impact factor is introduced and proved to be effective for reducing the convergence time in the RUACA performance. In order to assess the RUACA performance, a simply supported data set of cities, which was taken as the source data in previous research using traditional ACA and genetic algorithm(GA), is chosen as a benchmark case study. Comparing with the ACA and GA results, it is shown that the presented RUACA has successfully solved the TSP problem. The results of the proposed algorithm are found to be satisfactory.

Simulation of English text recognition model based on ant colony algorithm and genetic algorithm

2021 ◽  
pp. 1-12
Author(s):  
Fei Long
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Text Recognition ◽  
English Text ◽  
Recognition Model ◽  
Central Node ◽  
Model Based ◽  
Characteristic Points

The difficulty of English text recognition lies in fuzzy image text classification and part-of-speech classification. Traditional models have a high error rate in English text recognition. In order to improve the effect of English text recognition, guided by machine learning ideas, this paper combines ant colony algorithm and genetic algorithm to construct an English text recognition model based on machine learning. Moreover, based on the characteristics of ant colony intelligent algorithm optimization, a method of using ant colony algorithm to solve the central node is proposed. In addition, this paper uses the ant colony algorithm to obtain the characteristic points in the study area and determine a reasonable number, and then combine the uniform grid to select some non-characteristic points as the central node of the core function, and finally use the central node with a reasonable distribution for modeling. Finally, this paper designs experiments to verify the performance of the model constructed in this paper and combines mathematical statistics to visually display the experimental results using tables and graphs. The research results show that the performance of the model constructed in this paper is good.

A Hybrid Genetic Algorithm for Optimization of Two-Dimensional Cutting-Stock Problem

2012 ◽  
pp. 56-71
Author(s):  
Ahmed Mellouli ◽  
Faouzi Masmoudi ◽  
Imed Kacem ◽  
Mohamed Haddar
Keyword(s):  
Genetic Algorithm ◽  
Linear Programming ◽  
Production Cost ◽  
Programming Model ◽  
Hybrid Genetic Algorithm ◽  
Cutting Stock Problem ◽  
Cutting Stock ◽  
Two Dimensional ◽  
Genetic Approach ◽  
Cutting Pattern

In this paper, the authors present a hybrid genetic approach for the two-dimensional rectangular guillotine oriented cutting-stock problem. In this method, the genetic algorithm is used to select a set of cutting patterns while the linear programming model permits one to create the lengths to produce with each cutting pattern to fulfill the customer orders with minimal production cost. The effectiveness of the hybrid genetic approach has been evaluated through a set of instances which are both randomly generated and collected from the literature.

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