Scheduling of Cellular Manufacturing With Flexible Routes Intercell Moves for Carbon Reduction in a Network Environment

Author(s):  
Jialiang Liu ◽  
Qiong Liu ◽  
Chenxin Xu ◽  
Zhaorui Dong ◽  
Mengbang Zou

Abstract In order to rapidly share manufacturing resources among enterprises in a network environment, reduce carbon emissions and production costs, scheduling of cellular manufacturing with intercell moves is studied. Previous researches on cellular manufacturing with intercell moves either supposed that a part can only move between two cells at most one time or supposed that intercell moves of parts were on fixed paths. However, there might be several manufacturing cells with the same processing function or several same machines in different cells in a network environment. Intercell moves of parts might have flexible routes. To make the cellular manufacturing with intercell moves in a network environment, a scheduling model aiming at minimizing total carbon emissions, makespan and total costs is proposed for intercell moves with flexible routes and no restrictions on the number of intercell moves. An improved artificial bee colony algorithm (ABC) is proposed to solve the scheduling model. In order to improve searching ability of ABC, neighborhood search with an adaptive stepsize mechanism is proposed in leader bee phase and onlooker phase of the algorithm. A binary tournament selection method is designed to improve convergence speed in the onlooker bee phase. A case study is used to verify the proposed model and algorithm. The results show that improved algorithm has better performance on convergence speed and searching ability than that of original artificial bee colony algorithm.

2020 ◽  
Vol 309 ◽  
pp. 03012 ◽  
Author(s):  
Bibo Hu

In this paper, through the analysis of the artificial intelligence algorithm, shuffled frog leaping algorithm is effectively improved, and the position of the frog is determined by the quantum rotation angle, so as to improve the performance of the algorithm. Compared with the artificial bee colony algorithm and the shuffled frog leaping algorithm, the improved algorithm has a significant improvement in the convergence speed of the algorithm and the ability to jump out of the local area.


2016 ◽  
Vol 25 (02) ◽  
pp. 1550034 ◽  
Author(s):  
Habib Ghafarzadeh ◽  
Asgarali Bouyer

Data clustering is a common data mining techniques used in many applications such as data analysis and pattern recognition. K-means algorithm is the common clustering method which has fallen into the trap of local optimization and does not always create the optimized response to the problem, although having more advantages such as high speed. Artificial bee colony (ABC) is a novel biological-inspired optimization algorithm, having the advantage of less control parameters, strong global optimization ability and easy to implement. However, there are still some problems in ABC algorithm, like inability to find the best solution from all possible solutions. Due to the large step of searching equation in ABC, the chance of skipping the true solution is high. Therefore, in this paper, to balance the diversity and convergence ability of the ABC, Mantegna Lévy distribution random walk is proposed and incorporated with ABC. The new algorithm, ABCL, brings the power of the Artificial Bee Colony algorithm to the K-means algorithm. The proposed algorithm benefits from Mantegna Lévy distribution to promote the ABC algorithm in solving the number of functional evaluation and also obtaining better convergence speed and high accuracy in a short time. We empirically evaluate the performance of our proposed method on nine standard datasets taken from the UCI Machine Learning Repository. The experimental results show that the proposed algorithm has ability to obtain better results in terms of convergence speed, accuracy, and reducing the number of functional evaluation.


2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Wang Chun-Feng ◽  
Liu Kui ◽  
Shen Pei-Ping

Artificial bee colony (ABC) algorithm is one of the most recent swarm intelligence based algorithms, which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To overcome this problem, we propose a novel artificial bee colony algorithm based on particle swarm search mechanism. In this algorithm, for improving the convergence speed, the initial population is generated by using good point set theory rather than random selection firstly. Secondly, in order to enhance the exploitation ability, the employed bee, onlookers, and scouts utilize the mechanism of PSO to search new candidate solutions. Finally, for further improving the searching ability, the chaotic search operator is adopted in the best solution of the current iteration. Our algorithm is tested on some well-known benchmark functions and compared with other algorithms. Results show that our algorithm has good performance.


2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gan Yu ◽  
Hongzhi Zhou ◽  
Hui Wang

To accelerate the convergence speed of Artificial Bee Colony (ABC) algorithm, this paper proposes a Dynamic Reduction (DR) strategy for dimension perturbation. In the standard ABC, a new solution (food source) is obtained by modifying one dimension of its parent solution. Based on one-dimensional perturbation, both new solutions and their parent solutions have high similarities. This will easily cause slow convergence speed. In our DR strategy, the number of dimension perturbations is assigned a large value at the initial search stage. More dimension perturbations can result in larger differences between offspring and their parent solutions. With the growth of iterations, the number of dimension perturbations dynamically decreases. Less dimension perturbations can reduce the dissimilarities between offspring and their parent solutions. Based on the DR, it can achieve a balance between exploration and exploitation by dynamically changing the number of dimension perturbations. To validate the proposed DR strategy, we embed it into the standard ABC and three well-known ABC variants. Experimental study shows that the proposed DR strategy can efficiently accelerate the convergence and improve the accuracy of solutions.


2020 ◽  
Vol 38 (9A) ◽  
pp. 1384-1395
Author(s):  
Rakaa T. Kamil ◽  
Mohamed J. Mohamed ◽  
Bashra K. Oleiwi

A modified version of the artificial Bee Colony Algorithm (ABC) was suggested namely Adaptive Dimension Limit- Artificial Bee Colony Algorithm (ADL-ABC). To determine the optimum global path for mobile robot that satisfies the chosen criteria for shortest distance and collision–free with circular shaped static obstacles on robot environment. The cubic polynomial connects the start point to the end point through three via points used, so the generated paths are smooth and achievable by the robot. Two case studies (or scenarios) are presented in this task and comparative research (or study) is adopted between two algorithm’s results in order to evaluate the performance of the suggested algorithm. The results of the simulation showed that modified parameter (dynamic control limit) is avoiding static number of limit which excludes unnecessary Iteration, so it can find solution with minimum number of iterations and less computational time. From tables of result if there is an equal distance along the path such as in case A (14.490, 14.459) unit, there will be a reduction in time approximately to halve at percentage 5%.


2013 ◽  
Vol 32 (12) ◽  
pp. 3326-3330
Author(s):  
Yin-xue ZHANG ◽  
Xue-min TIAN ◽  
Yu-ping CAO

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