检索结果-内蒙古大学图书馆

A Metaheuristic algorithm for the manufacturing cell formation problem based on grouping efficacy

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第1-4期82卷 25-37页

作者： Noktehdan, Azadeh Seyedhosseini, Seyedmohammad Saidi-Mehrabad, Mohammad Iran Univ Sci & Technol Dept Ind Engn Tehran Iran

The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers.

关键词： Cell formation problem grouping genetic algorithm League championship algorithm

来源：评论

学校读者我要写书评

暂无评论

Assembly line balancing in garment industry

引用

EXPERT SYSTEMS WITH APPLICATIONS 2012年第11期39卷 10073-10081页

作者： Chen, James C. Chen, Chun-Chieh Su, Ling-Huey Wu, Han-Bin Sun, Cheng-Ju Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu Taiwan Chung Yuan Christian Univ Dept Ind & Syst Engn Chungli Taiwan

Garment manufacturing is a traditional industry with global competition. The most critical manufacturing process is sewing, as it generally involves a great number of operations. The aim of assembly line balance planning in sewing lines is to assign tasks to the workstations, so that the machines of the workstation can perform the assigned tasks with a balanced loading. Assembly line balancing problem (ALBP) is known as an NP-hard problem. Thus, the heuristic methodology could be a better way to plan the sewing lines within a reasonable time. This paper develops a grouping genetic algorithm (GGA) for ALBP of sewing lines with different labor skill levels in garment industry. GGA can allocate workload among machines as evenly as possible for different labor skill levels, so the mean absolute deviations (MAD) can be minimized. Real data from garment factories and experimental design are used to evaluate GGA's performance. Production managers can use the research results to quickly design sewing lines for important targets such as short cycle time and high labor utilization. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Assembly line balancing problem grouping genetic algorithm Garment industry Labor skill level

来源：评论

学校读者我要写书评

暂无评论

grouping evolution strategies: An effective approach for grouping problems

引用

APPLIED MATHEMATICAL MODELLING 2015年第9期39卷 2703-2720页

作者： Kashan, Ali Husseinzadeh Akbari, Ali Akbar Ostadi, Bakhtiar Tarbiat Modares Univ Fac Engn Dept Ind Engn Tehran Iran Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Many combinatorial optimization problems include a grouping (or assignment) phase wherein a set of items are partitioned into disjoint groups or sets. Introduced in 1994, the grouping genetic algorithm (GGA) is the most established heuristic for grouping problems which exploits the structural information along with the grouping nature of these problems to steer the search process. The aim of this paper is to evaluate the grouping version of the classic evolution strategies (ES) which originally maintain the well-known Gaussian mutation, recombination and selection operators for optimizing non-linear real-valued functions. Introducing the grouping evolution strategies (GES) to optimize the grouping problems that are intrinsically discrete, requests for developing a new mutation operator which works with groups of items rather than scalars and is respondent to the structure of grouping problems. As a source of variation, GES employs a mutation operator which shares a same rationale with the original ES mutation in the way that it works in continuous space while the consequences are used in discrete search space. A two phase heuristic procedure is developed to generate a complete feasible solution from the output of the mutation process. An extensive comparative study is conducted to evaluate the performance of GES versus GGA and GPSO (a recently proposed grouping particle swarm optimization algorithm) on test problem instances of the single batch-processing machine scheduling problem and the bin-packing problem. While these problems share exactly a same grouping structure and the performance of GES on both problems is reliable, switching from one problem to another deteriorates the performance of GGA. Though such a deficiency is not observed in the performance of GPSO, it is still inferior to GES on the single batch-processing machine scheduling test problem instances. Beside such empirical outcomes, the paper conveys a number of core strengths that the design of GES

关键词： grouping problems grouping genetic algorithm grouping evolution strategies grouping particle swarm optimization Batch-processing machine scheduling problem Bin-packing problem

来源：评论

学校读者我要写书评

暂无评论

A novel discrete particle swarm optimization algorithm for the manufacturing cell formation problem

引用

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第9-12期73卷 1543-1556页

作者： Kashan, Ali Husseinzadeh Karimi, Behrooz Noktehdan, Azadeh Tarbiat Modares Univ Dept Ind Engn Fac Engn Tehran Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran IUST Dept Ind Engn Tehran Iran

The manufacturing cell formation problem, with the aim of grouping parts into families and machines into cells, is considered with the objective of maximizing grouping efficacy. A new solution approach based on the particle swarm optimization (PSO) algorithm is presented for the problem. Unlike the original PSO algorithm which works with arithmetic operators and scalars, the new algorithm uses group-based operators, in place of arithmetic operators, in the body of the updating equations analogous to those of the classical PSO equations (given the fact that the cell formation problem is essentially a grouping problem, all operators in the new algorithm work with constructed cells (groups) rather than parts/machines (objects), isolatedly). We benchmark a set of 40 test problem instances from previous researches and do comparisons between the new algorithm and existing algorithms. We also compare the performance of our algorithm when it is hybridized with a local search module. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution's quality presented in the literature.

关键词： Group technology Cell formation problem grouping genetic algorithm Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters

引用

IEEE TRANSACTIONS ON SERVICES COMPUTING 2019年第4期12卷 550-563页

作者： Wu, Quanwang Ishikawa, Fuyuki Zhu, Qingsheng Xia, Yunni Chongqing Univ Comp Coll Chongqing 400044 Peoples R China Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan

Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.

关键词： Virtual machine consolidation energy migration cost heterogeneity grouping genetic algorithm

来源：评论

学校读者我要写书评

暂无评论

grouping efficiency measures and their impact on factory measures for the machine-part cell formation problem: A simulation study

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2007年第1期20卷 63-78页

作者： Keeling, Kellie B. Brown, Evelyn C. James, Tabitha L. Virginia Polytech Inst & State Univ RB Pamplin Coll Business Dept Business Informat Technol Blacksburg VA 24061 USA E Carolina Univ Dept Engn Greenville NC 27858 USA

Over the past 25 years, the machine-part cell formation problem has been the subject of numerous studies. Researchers have applied various methodologies to the problem in an effort to determine optimal clusterings of machines and optimal groupings of parts into families. The quality of these machine and part groupings have been evaluated using various objective functions, including grouping efficacy, grouping index, grouping capability index, and doubly weighted grouping efficiency, among others. In this study, we investigate how appropriate these grouping quality measures are in determining cell formations that optimize factory performance. Through the application of a grouping genetic algorithm, we determine machine/part cell formations for several problems from the literature. These cell formations are then simulated to determine their impact on various factory measures, such as flow time, wait time, throughput, and machine utilization, among others. Results indicate that it is not always the case that a "more efficient" machine/part cell formation leads to significant changes or improvements in factory measures over a "less efficient" cell formation. In other words, although researchers are working to optimize cell formations using efficiency measures, cells formed this way do not always demonstrate optimized factory measures. (C) 2006 Elsevier Ltd. All rights reserved.

关键词： grouping genetic algorithm grouping efficacy grouping capability index

来源：评论

学校读者我要写书评

暂无评论

A differential evolution algorithm for the manufacturing cell formation problem using group based operators

引用

EXPERT SYSTEMS WITH APPLICATIONS 2010年第7期37卷 4822-4829页

作者： Noktehdan, Azadeh Karimi, Behrooz Kashan, Ali Husseinzadeh Amirkabir Univ Technol Dept Ind Engn Tehran Iran

Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differential evolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored with a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Cell formation problem grouping genetic algorithm Differential evolution algorithm

来源：评论

学校读者我要写书评

暂无评论

Two heuristics for the one-dimensional bin-packing problem

引用

OR SPECTRUM 2007年第4期29卷 765-781页

作者： Singh, Alok Gupta, Ashok K. Univ Allahabad Fac Sci J K Inst Appl Phys & Technol Allahabad 211002 Uttar Pradesh India

In this paper we have proposed two heuristics for the one-dimensional bin-packing problem. One is a hybrid steady-state grouping genetic algorithm, whereas the other is an improved version of Perturbation MBS' heuristic (Fleszar and Hindi in Comput Oper Res 29:821-839, 2002). A combined approach using these two heuristics gives results which are comparable with the best methods known so far.

关键词： combinatorial optimization grouping genetic algorithm heuristic one-dimensional bin-packing

来源：评论

学校读者我要写书评

暂无评论

Unsupervised feature selection algorithm for multiclass cancer classification of gene expression RNA-Seq data

引用

GENOMICS 2020年第2期112卷 1916-1925页

作者： Garcia-Diaz, Pilar Sanchez-Berriel, Isabel Martinez-Rojas, Juan A. Diez-Pascual, Ana M. Univ Alcala Polytech Sch Dept Signal Theory & Commun Madrid 28805 Spain Univ La Laguna Higher Sch Engn & Technol Dept Comp & Syst Engn San Cristobal La Laguna 38200 Sc De Tenerife Spain Univ Alcala Fac Sci Dept Analyt Chem Phys Chem & Chem Engn Madrid 28805 Spain

This paper presents a grouping genetic algorithm (GGA) to solve a maximally diverse grouping problem. It has been applied for the classification of an unbalanced database of 801 samples of gene expression RNA-Seq data in 5 types of cancer. The samples are composed by 20,531 genes. GGA extracts several groups of genes that achieve high accuracy in multiple classification. Accuracy has been evaluated by an Extreme Learning Machine algorithm and was found to be slightly higher in balanced databases than in unbalanced ones. The final classification decision has been made through a weighted majority vote system between the groups of features. The proposed algorithm finally selects 49 genes to classify samples with an average accuracy of 98.81% and a standard deviation of 0.0174.

关键词： Gene expression cancer Feature selection Multi-classification grouping genetic algorithm Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

Optimizing Diverse Group Stock Portfolio without Setting A Number of Groups 31

Optimizing Diverse Group Stock Portfolio without Setting A N...

引用

31st IEEE International Conference on Advanced Information Networking and Applications (IEEE AINA)

作者： Chen, Chun-Hao Coupe, Jonathan Hong, Tzung-Pei Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Univ Kaohsiung Dept Comp Sci & Informat Engn Kaohsiung Taiwan Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (纸本)9781509060290

In the previous approach, by grouping genetic algorithm, an approach has been proposed for mining diverse group stock portfolio which can be used to generate various stock portfolios. However, a parameter, number of groups, should be given in advanced of that approach, and it is hard to set appropriate value. Hence, the intent of this research is to design an algorithm that can find appropriate number of groups automatically and a better diverse group stock portfolio from a given financial dataset. To allow exploration of different group sizes, the Davis-Bouldin index is considered to develop the fitness function for chromosome evaluation. In addition, the split and merge genetic operators are also utilized to increase and reduce the group sizes of a chromosome. Experiments on a real dataset were conducted to show the effectiveness of the proposed approach.

关键词： grouping genetic algorithm portfolio optimization group stock portfolio grouping problem cluster validation index

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：