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U-shaped assembly line worker assignment and balancing problem: A mathematical model and two meta-heuristics

COMPUTERS & INDUSTRIAL ENGINEERING 2017年 112卷 246-263页

作者： Oksuz, Mehmet Kursat Buyukozkan, Kadir Satoglu, Sule Itir Istanbul Tech Univ Fac Management Ind Engn Dept TR-34367 Istanbul Turkey Karadeniz Tech Univ Fac Engn Ind Engn Dept Kanuni Campus Trabzon Turkey

As the U-shaped assembly lines are concerned with manual assembly operations, workers' performance plays a major role. However, the past studies assume that all workers can carry out all tasks with an equal performance level, and actual task times do not depend on the worker who performs it. On the other hand, this is unrealistic. In this study, maximization of the line efficiency for the U-shaped assembly line worker assignment and balancing problem by considering the worker performance is aimed. First, a non-linear model of the problem is formulated, and then it is linearized. Later, an artificial bee colony algorithm and a Genetic algorithm are proposed to solve it, in short computational times. A hypothetical data set (Jackson, 1956), a real data set (Cevikcan et al., 2009) and data sets of assembly line worker assignment and balancing problem (Araujo et al., 2015) were solved by using the model and the meta heuristics. The meta-heuristics were able to reach solutions that have equal line efficiency with the optimum solution found by the proposed mathematical model. The contribution of this study is that the mathematical model for the U-shaped line assembly line worker assignment and balancing type-E problem that considers the workers' performance is formulated, and an artificial bee colony algorithm is proposed for the first time for this problem type. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： U-shaped assembly line Worker assignment Worker performance Heterogeneous workers Integer programming artificial bee colony algorithm Genetic algorithm

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Pulmonary nodules computer-aided diagnosis based on feature integration and ABC-LVQ network

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2018年第6期9卷 577-589页

作者： Zhao, Qing-Shan Ji, Guo-Hua Hu, Yu-Lan Meng, Guo-Yan Xinzhou Teachers Univ Dept Comp Sci & Technol Xinzhou 034000 Shanxi Peoples R China Xinzhou Teachers Univ Dept Math Xinzhou 034000 Shanxi Peoples R China

For the computer aided diagnosis of lung cancer, a malignancy identification method based on multi-feature integration and learning vector quantisation (LVQ) network optimised by artificial bee colony (ABC) is proposed in this work. Firstly, the traditional features and the hidden features learned by Sparse Autoencoder of nodules are respectively extracted, and then the canonical correlation analysis (CCA) is used for feature integration. For classification, the ABC algorithm is used to optimise the LVQ network to overcome its sensitivity to initial value. Finally, the integrated features of nodules are input into the optimised classifier and the diagnosis results are obtained. Experimental results on LIDC pulmonary nodule image datasets show that this method can effectively identify the malignancy of nodules, with the area under the receiver operating characteristic (ROC) curve (AUC) of 0.90, 0.83, 0.80, 0.80, 0.85 for nodules of malignancy 1-5 classification, respectively.

关键词： pulmonary nodules computer aided diagnosis learning vector quantisation network LVQ artificial bee colony algorithm ABC feature integration

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Hybrid metaheuristic approaches for the single machine total stepwise tardiness problem with release dates

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OPERATIONAL RESEARCH 2017年第1期17卷 275-295页

作者： Chaurasia, Sachchida Nand Sundar, Shyam Singh, Alok Univ Hyderabad Sch Comp & Informat Sci Hyderabad 500046 Andhra Prades India Natl Inst Technol Raipur Dept Comp Applicat GE Rd Raipur 492010 Madhya Pradesh India

This paper presents two hybrid metaheuristic approaches, viz. a hybrid genetic algorithm and a hybrid artificial bee colony algorithm for a single machine scheduling problem where tardiness cost of a job increases stepwise with various due dates and the objective is to minimize the total tardiness cost. This kind of tardiness cost occurs in several real life scenarios particularly in transportation. Two versions of the scheduling problem are considered. In the first version, all jobs are assumed to be available for processing at the beginning, whereas in the latter version jobs have release dates. For both versions, we have employed a local search to further improve the solutions obtained through our metaheuristic approaches. To the best of our knowledge, our approaches are the first metaheuristic approaches for the latter version of the problem. For the first version, we have compared our approaches with the state-of-the-art approaches available in the literature. Computational results show the superiority of our approaches over previous approaches in terms of solution quality and running time both. For the latter version, hybrid artificial bee colony algorithm based approach outperformed the hybrid genetic algorithm based approach in terms of solution quality and running time both.

关键词： Single machine total stepwise tardiness problem artificial bee colony algorithm Genetic algorithm Heuristic Release dates Scheduling

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Web Page Interface Optimization Based on Nature-Inspired algorithms

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2018年第2期9卷 28-46页

作者： Sakulin, Sergey Alfimtsev, Alexander Solovyev, Dmitry Sokolov, Dmitry Bauman Moscow State Tech Univ Dept IU 3 Moscow Russia Bauman Moscow State Tech Univ Informat Syst & Telecommun Moscow Russia Bauman Moscow State Tech Univ Informat & Control Syst Fac Moscow Russia Bauman Moscow State Tech Univ Moscow Russia

This article describes how the conversion rate of a web page depends on the interface usability degree. Optimization of existing interfaces as the matter of improving their usability faces a number of difficulties. In the first place, the unified objective function selection method for such optimization is not set up;that is resulting in necessity of qualified experts' participation for its implementation. In the second place, the corresponding optimization problem will have a high dimension, which makes the classical optimization methods unsuitable for the problem solution. Nature-inspired algorithms have undeniable advantages in comparison with classical optimization algorithms for solving high-dimensional problems, such as for example the optimization of web interfaces by their usability criterion. In this article, new web page interface optimization methods based on nature-inspired algorithms are proposed. In particular, genetic algorithms (GAs), artificial bee colony algorithms (ABC), and charged system search algorithms (CSSs) were analyzed. The conducted experiments revealed the advantages of these algorithms for posed problem solutions and showed research prospects in this direction.

关键词： artificial bee colony algorithm Charged System Search algorithm Genetic algorithm Interface Heat Maps Usability Optimization User Experience Web Interface

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Minimizing average lead time for the coordinated scheduling problem in a two-stage supply chain with multiple customers and multiple manufacturers

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COMPUTERS & INDUSTRIAL ENGINEERING 2017年 114卷 244-257页

作者： Yilmaz, Omer Faruk Pardalos, Pans M. Yalova Univ Dept Ind Engn TR-77200 Yalova Turkey Univ Florida Dept Ind & Syst Engn Ctr Appl Optimizat Gainesville FL 32611 USA Natl Res Univ Higher Sch Econ Lab Algorithms & Technol Network Anal Nizhnii Novgorod Russia

In this study, the two-stage supply chain scheduling problem with multiple customers and multiple manufacturers is considered. The first stage consists of m manufacturers (suppliers), while the second stage contains q vehicles, each of which distributes the batches from the manufacturers to the customers. Multiple customers and average lead time are two most important issues in practice;however, no study has been carried out so far to investigate these two issues together for the two-stage supply chain scheduling problem. The main contribution of this study is to coordinate production and distribution decisions to obtain an effective scheduling in a two stage supply chain that contains multiple customers and multiple manufacturers. A mixed integer linear optimization model is developed to formulate the problem with the average lead time objective. Because the problem has been shown-to be NP-hard, a hybrid artificial bee colony and simulated annealing (HABCSA) algorithm is introduced and used to solve the problem. In addition, a lower bound (LB) and several structural properties for the problem are presented and different batching mechanisms are developed based on these properties. For the purpose of performance analysis of HABCSA with different batching mechanisms, detailed computational experiments are conducted using random instances which are generated based on real aluminum production data for different capacity levels. The experimental results indicate that the HABCSA heuristic consistently outperforms the Genetic algorithm (GA) and the artificial bee colony (ABC) algorithm for each capacity level.

关键词： Two-stage supply chain scheduling artificial bee colony algorithm Simulated annealing algorithm Retching mechanisms Average lead time

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Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values

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COMPUTATIONAL GEOSCIENCES 2017年第4期21卷 665-681页

作者： Fattahi, Hadi Arak Univ Technol Dept Min Engn Arak *** Iran

The uniaxial compressive strength (UCS) of rock is widely used in designing underground and surface rock structures. The testing procedure of this rock strength is expensive and time consuming. In addition, it requires well-prepared rock cores. Therefore, indirect tests are often used to estimate the UCS, such as the Schmidt hammer test. This test is very easy to carry out because it necessitates less or no sample preparation and the testing equipment is less sophisticated. In addition, it can be used easily in the field. As a result, comparing with uniaxial compression test, indirect test is simpler, faster, and more economical. In this paper, the application of soft computing methods for data analysis named support vector regression (SVR) optimized by artificial bee colony algorithm (ABC) and adaptive neuro-fuzzy inference system-subtractive clustering method (ANFIS-SCM) to estimate the UCS of rocks from Schmidt hammer rebound values is demonstrated. The estimation abilities offered using SVR-ABC and ANFIS-SCM were presented by using experimental data given in open-source literatures. In these models, the Schmidt hammer rebound values (T1-T3, R1-R4) were utilized as the input parameters, while the UCS was the output parameter. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential to indirect estimation of the UCS of rocks from the Schmidt hammer rebound values with high degree of accuracy and robustness.

关键词： Uniaxial compressive strength Schmidt hammer rebound values Support vector regression artificial bee colony algorithm ANFIS-subtractive clustering method

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Integrated Speaker and Speech Recognition for Wheel Chair Movement using artificial Intelligence

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INFORMATICA-JOURNAL OF COMPUTING AND INFORMATICS 2018年第4期42卷 587-594页

作者： Kaur, Gurpreet Srivastava, Mohit Kumar, Amod IK Gujral Punjab Tech Univ Kapurthala 144603 India Panjab Univ Univ Inst Engn & Technol Chandigarh 160025 India Chandigarh Engn Coll Landran 140307 Mohali India Cent Sci Instruments Org Chandigarh 160030 India

A speech signal is a result of the constrictions of vocal tract and different sounds can be generated by different vocal tract constrictions. A speech signal carries two things i.e. speaker's identity and meaning. For a specific applications of speaker and speech recognition like voice operated wheel chair, both the speaker and speech is to be recognized for movement of wheel chair. Automation in wheelchair is, today's requirement as the numbers of people are increasing with disabilities like injuries in spine, amputation, impairments in hands etc. They need assistance for moving their wheel chair. Voice operated wheel chair is one of the solution. The intention of this study is to use a speaker and speech dependent system to control the wheelchair and minimize the risk of unwanted accident. We have proposed a system in which speaker (patient) as well as speech (commands) is recognized based upon the acoustic features like Mel Frequency Cepstral Coefficients (MFCC). Optimization of the features using artificial bee algorithm (ABC) is done to gain good accuracy with artificial intelligence technique as a classifier. We have tested our system on standard dataset (TIDIGITS) and our own prepared dataset. Also, validation of proposed work is done by generating control signal to actuate the wheel chair in real time scenario.

关键词： speaker recognition speech recognition mel frequency cepstral coefficients artificial bee colony algorithm feed forward back propagation neural network

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2D Direction Histogram-Based Renyi Entropic Multilevel Thresholding

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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2018年第3期22卷 369-379页

作者： Yimit, Adiljan Hagihara, Yoshihiro Iwate Univ Fac Sci & Engn 4-3-5 Ueda Morioka Iwate 0208551 Japan

2D histogram-based thresholding methods, in which the histogram is computed from local image features, have better performance than 1D histogram-based methods, but they take much more computation time. In this paper, we present a Renyi entropic multilevel thresholding (REMT) method based on a 2D direction histogram constructed from pixel values and local directional features. In addition to presenting a fast recursive method for REMT, we propose the Renyi entropic artificial bee colony multilevel thresholding (REABCMT) method to quickly find the optimal threshold values. In order to demonstrate the efficacy of REABCMT, three versions of this method are compared in terms of computation time and optimal threshold values. In addition, the segmentation performance of REABCMT is also evaluated by comparing it with two other methods to show its effectiveness. Moreover, in order to evaluate the efficiency and stability of using the ABC algorithm in the search for threshold values, genetic algorithm (GA) and particle swarm optimization (PSO), two common optimization algorithms, are also compared with it.

关键词： entropic thresholding segmentation 2D direction histogram Renyi entropy artificial bee colony algorithm

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An Augmented artificial bee colony with Hybrid Learning for Traveling Salesman Problem 12th

An Augmented Artificial Bee Colony with Hybrid Learning for ...

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12th International Conference on Intelligent Computing (ICIC)

作者： Hu, Guozheng Chu, Xianghua Niu, Ben Li, Li Lin, Dechang Liu, Yao Shenzhen Univ Coll Management Shenzhen Peoples R China Guangdong Pharmaceut Univ Med Business Sch Guangzhou Guangdong Peoples R China

ISBN: (纸本)9783319422916;9783319422909

Traveling salesman problem (TSP) is a renowned NP-hard combinatorial optimization model which widely studied in the operation research community, such as transportation, logistics and industries areas. To address the problem effectively and efficiently, in this paper, a new meta-heuristic method, named hybrid learning artificial bee colony, is proposed based on the simply yet powerful swarm intelligence method, artificial bee colony algorithm. In HLABC, two different learning strategies are adopted in the employed bee phase and the onlooker bee phase. The updating mechanism for food source position is enhanced by employing global best food source. Experimental results on TSP problems with various city sizes indicate the effectiveness of the proposed algorithm.

关键词： Traveling salesman problem artificial bee colony algorithm Hybrid learning

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Optimal Fast Charging Station Location for Public Electric Transportation in Smart Power Distribution Network

Optimal Fast Charging Station Location for Public Electric T...

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International Electrical Engineering Congress (iEECON)

作者： Terapong Boonraksa Boonruang Marungsri School of Electrical Engineering Suranaree University of Technology Nakhon Ratchasima Thailand

ISBN: (纸本)9781538623183;9781538623176

Electric vehicle (EV) become increasingly active in public transportation system. The battery and the charging system to keep it running are essential. For tech-economic benefits on the installation of the charging station, the location design is an important part. Include management to make it appropriate for the radial distribution system. This article presents an analysis to determine the optimal charging station for the radial distribution system. By simulating the charging station according to IEEE 33 bus standards. The artificial bee colony (ABC) algorithm is used to find the location of the best charging station for use with the radial distribution system. The results show that the proposed method finds the optimal charging station for EV. It can reduce the cost of creating additional locations including a suitable supply of the electrical system and it can also be applied to transportation systems within the city or between cities.

关键词： Charging stations Electric vehicles artificial bee colony algorithm Batteries Power distribution

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