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grouping genetic algorithms: an efficient method to solve the cell formation problem

MATHEMATICS AND COMPUTERS IN SIMULATION 2000年第3-4期51卷 257-271页

作者： De Lit, P Falkenauer, E Delchambre, A Free Univ Brussels Dept Appl Mech Brussels Belgium

The layout problem arises in a production plant during the study of a new production system, but also during a possible restructuring. The main aim of layout design is to reduce transportation and maintenance, which simplifies management, shortens lead time, improves product quality and speeds up the response to market fluctuations. A principle of Group Technology (GT) advocates the division of a unity into small groups or cells. As it is most of the time impossible to design totally independent cells, the problem is to minimise traffic of items between the cells, for a fixed maximum cell size. This problem is known as cell formation problem (CFP). We propose here an original approach to solve this NP-hard problem. It is based on a grouping genetic Algorithm (GGA), a special class of genetic algorithms, heavily modified to suit the structure of grouping problems. The crucial advantage of this GGA is that it is able to deal with large instances of the problem thus becoming a powerful tool for an engineer determining a plant layout, allowing him or her to try several plant options, without the limitation of huge computation times. (C) 2000 IMACS/Elsevier Science B.V. All rights reserved.

关键词： grouping genetic algorithms cell formation and decomposition group technology

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grouping genetic algorithms: An Exploratory Study

Grouping Genetic Algorithms: An Exploratory Study

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World Congress on Engineering and Computer Science, WCES 2017

作者： Mutingi, Michael Mbohwa, Charles Musiyarira, Harmony IAENG Hong Kong Hong Kong Peoples R China Namibia Univ Sci & Technol Fac Engn Windhoek Namibia Univ Johannesburg Fac Engn & Built Environm Dept Qual & Operat Management Johannesburg South Africa

ISBN: (纸本)9789881404756

grouping problems are an important class of computational problems where the objective is to group, cluster or partition members of a set into desired sub sets. The grouping genetic algorithm is an extension of genetic algorithm that is heavily modified to model the structure of grouping problems. Since its inception, grouping genetic algorithms has been applied to several types of grouping problems. This paper presents an exploratory and chronological review of the grouping genetic algorithm approach. First, a survey of articles on grouping genetic algorithm approaches and its applications is presented. Second, a chronological review and analysis of research activities on the grouping genetic algorithm approach is presented. Third and finally, future trends and further research prospects are visualized and outlined.

关键词： genetic algorithms grouping grouping genetic algorithms exploratory study chronological review

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Optimization of one-dimensional Bin Packing Problem with island parallel grouping genetic algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 2014年第1期75卷 176-186页

作者： Dokeroglu, Tansel Cosar, Ahmet Middle E Tech Univ Dept Comp Engn TR-06800 Ankara Turkey

The well-known one-dimensional Bin Packing Problem (BPP) of whose variants arise in many real life situations is a challenging NP-Hard combinatorial optimization problem. Metaheuristics are widely used optimization tools to find (near-) optimal solutions for solving large problem instances of BPP in reasonable running times. With this study, we propose a set of robust and scalable hybrid parallel algorithms that take advantage of parallel computation techniques, evolutionary grouping genetic metaheuristics, and bin-oriented heuristics to obtain solutions for large scale one-dimensional BPP instances. A total number of 1318 benchmark problems are examined with the proposed algorithms and it is shown that optimal solutions for 88.5% of these instances can be obtained with practical optimization times while solving the rest of the problems with no more than one extra bin. When the results are compared with the existing state-of-the-art heuristics, the developed parallel hybrid grouping genetic algorithms can be considered as one of the best one-dimensional BPP algorithms in terms of computation time and solution quality. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： One-dimensional bin packing grouping genetic algorithms Parallel computation Hybrid-heuristics

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Mining Group Stock Portfolio by Using grouping genetic algorithms

Mining Group Stock Portfolio by Using Grouping Genetic Algor...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Chen, Chun-Hao Lin, Cheng-Bon Chen, Chao-Chun Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan Natl Cheng Kung Univ Inst Mfg Informat & Syst Dept Comp Sci & Informat Engn Tainan Taiwan

ISBN: (纸本)9781479974924

In this paper, a grouping genetic algorithm based approach is proposed for dividing stocks into groups and mining a set of stock portfolios, namely group stock portfolio. Each chromosome consists of three parts. grouping and stock parts are used to indicate how to divide stocks into groups. Stock portfolio part is used to represent the purchased stocks and their purchased units. The fitness of each chromosome is evaluated by the group balance and the portfolio satisfaction. The group balance is utilized to make the groups represented by the chromosome have as similar number of stocks as possible. The portfolio satisfaction is used to evaluate the goodness of profits and satisfaction of investor's requests of all possible portfolio combinations that can generate from a chromosome. Experiments on a real data were also made to show the effectiveness of the proposed approach.

关键词： data mining grouping genetic algorithms genetic algorithms grouping problems stock portfolio optimization

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Team formation based on group technology: A hybrid grouping genetic algorithm approach

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COMPUTERS & OPERATIONS RESEARCH 2011年第2期38卷 484-495页

作者： Agustin-Blas, Luis E. Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Portilla-Figueras, Antonio Perez-Bellido, Angel M. Jimenez-Fernandez, Silvia Univ Alcala de Henares Grp Heurist Modernos Optimizac & Diseno Rede GHEO Dept Signal Theory & Commun Madrid 28871 Spain

This paper presents a new model for team formation based on group technology (TFPGT). Specifically, the model is applied as a generalization of the well-known Machine-Part Cell Formation problem, which has become a classical problem in manufacturing in the last few years. In this case, the model presented is especially well-suited for problems of team formation arising in R&D-oriented or teaching institutions. A parallel hybrid grouping genetic algorithm (HGGA) is also proposed in the paper to solve the TFPGT. The performance of the algorithm is shown in several synthetic TFPGT instances, and in a real problem: the formation of teaching groups at the Department of Signal Theory and Communications of the Universidad de Alcala in Spain. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Team formation Group technology grouping genetic algorithms Hybrid algorithms

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A new grouping genetic algorithm for clustering problems

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第10期39卷 9695-9703页

作者： Agustin-Blas, L. E. Salcedo-Sanz, S. Jimenez-Fernandez, S. Carro-Calvo, L. Del Ser, J. Portilla-Figueras, J. A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain Tecnalia Res & Innovat Bizkaia Spain

In this paper we present a novel grouping genetic algorithm for clustering problems. Though there have been different approaches that have analyzed the performance of several genetic and evolutionary algorithms in clustering, the grouping-based approach has not been, to our knowledge, tested in this problem yet. In this paper we fully describe the grouping genetic algorithm for clustering, starting with the proposed encoding, different modifications of crossover and mutation operators, and also the description of a local search and an island model included in the algorithm, to improve the algorithm's performance in the problem. We test the proposed grouping genetic algorithm in several experiments in synthetic and real data from public repositories, and compare its results with that of classical clustering approaches, such as K-means and DBSCAN algorithms, obtaining excellent results that confirm the goodness of the proposed grouping-based methodology. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： grouping genetic algorithms Clustering problems Hybrid algorithms

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A hybrid grouping genetic algorithm for assigning students to preferred laboratory groups

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第3期36卷 7234-7241页

作者： Agustin-Blas, Luis E. Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Portilla-Figueras, Antonio Perez-Bellido, Angel M. Univ Alcala de Henares Dept Signal Theory & Commun Grp Heurist Modernos Optimizac & Diseno Redes GHE Madrid 28871 Spain

This paper presents a novel application of the hybrid grouping genetic algorithm in a problem related to university timetabling. Specifically, the assignment of students to laboratory groups is tackled. This problem includes an important constraint of capacity, due to laboratories usually have a maximum number of equips or computers available. so the number of total students in a group is constrained to be equal or less than the capacity of the laboratory. In addition, our approach considers the case in which the students provide a sorted list of preferred laboratory groups, so the objective of the assignment must take this point into account. A variation of the problem in which a balanced number of students per group is required (lecturer preferences) is also studied in this paper. The performance of the approach is shown in different test problems and in a real application in a Spanish University. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Timetabling Combinatorial optimization grouping genetic algorithms Assignment problems

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MULTI-OBJECTIVE grouping genetic ALGORITHM FOR THE JOINT ORDER BATCHING, BATCH ASSIGNMENT, AND SEQUENCING PROBLEM

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2022年第3期17卷 188-204页

作者： Alejandro Cano, Jose Cortes, Pablo Antonio Campo, Emiro Correa-Espinal, Alexander A. Univ Medellin Fac Econ & Adm Sci Medellin Colombia Univ Seville Escuela Tecn Super Ingn Seville Spain Politecn Colombiano Jaime Isaza Cadavid Medellin Colombia Univ Nacl Colombia Fac Minas Medellin Colombia

This article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule-based heuristic and 10.2% better than an earliest due date (EDD) rule-based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives.

关键词： Order picking order batching batch assignment sequencing grouping genetic algorithms multi-objective problem joint order picking

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Hybrid evolutionary multi-objective algorithms for integrating assembly sequence planning and assembly line balancing

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2008年第21期46卷 5951-5977页

作者： Tseng, H. -E. Chen, M. -H. Chang, C. -C. Wang, W. -P. Natl Chin Yi Univ Technol Dept Ind Engn & Management Taiping City 411 Taichung County Taiwan Da Yeh Univ Dept Ind Engn Da Tsuen 515 Chang Hua Taiwan Huafan Univ Dept Ind Design Shihding Taipei County Taiwan

Assembly sequence planning (ASP) and assembly line balancing (ALB) play critical roles in designing product assembly systems. In view of the trend of concurrent engineering, pondering simultaneously over these two problems in the development of assembly systems is significant for establishing a manufacturing system. This paper contemplates the assembly tool change and the assembly direction as measurements in ASP;and further, Equal Piles assembly line strategy is adopted and the imbalanced status of the system employed as criteria for the evaluation concerning ALB. Focus of the paper is principally on proposing hybrid evolutionary multiple-objective algorithms (HEMOAs) for solutions with regard to integrate the evolutionary multi-objective optimization and grouping genetic algorithms. The results provide a set of objectives and amend Pareto-optimal solutions to benefit decision makers in the assembly plan. In addition, an implemented decision analytic model supports the preference selection from the Pareto-optimal ones. Finally, the exemplifications demonstrate the effectiveness and performance of the proposed algorithm. The consequences definitely illustrate that HEMOAs search out Pareto-optimal solutions effectively and contribute to references for the flexible change of assembly system design.

关键词： assembly sequence planning assembly line balancing pareto-optimal solutions grouping genetic algorithms hybrid evolutionary multiple-objective algorithms

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A novel grouping harmony search algorithm for the multiple-type access node location problem

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EXPERT SYSTEMS WITH APPLICATIONS 2012年第5期39卷 5262-5270页

作者： Landa-Torres, I. Gil-Lopez, S. Salcedo-Sanz, S. Del Ser, J. Portilla-Figueras, J. A. Univ Alcala Dept Signal Theory & Commun Madrid 28871 Spain TECNALIA TELECOM Zamudio Bizkaia Spain

In this paper we present a novel grouping harmony search algorithm for the Access Node Location Problem (ANLP) with different types of concentrators. The ANLP is a NP-hard problem where a set of distributed terminals, with distinct rate demands, must be assigned to a variable number of concentrators subject to capacity constraints. We consider the possibility of choosing between different concentrator models is given in order to provide service demand at different cost. The ANLP is relevant in communication networks design, and has been considered before within the design of MPLS networks, for example. The approach we propose to tackle the ANLP problem consists of a hybrid grouping Harmony Search (GHS) algorithm with a local search method and a technique for repairing unfeasible solutions. Moreover, the presented scheme also includes the adaptation of the GHS to a differential scheme, where each proposed harmony is obtained from the same harmony in the previous iteration. This differential scheme is perfectly adapted to the specifications of the ANLP problem, as it utilizes the grouping concept based on the proximity between nodes, instead of being only based on the grouping concept. This allows for a higher efficiency on the searching process of the algorithm. Extensive Monte Carlo simulations in synthetic instances show that this proposal provides faster convergence rate, less computational complexity and better statistical performance than alternative algorithms for the ANLP, such as grouping genetic algorithms, specially when the size of the scenario increases. We also include practical results for the application of GHS to a real wireless network deployment problem in Bizkaia, northern Spain. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Harmony search algorithm Access node location problem grouping-based encoding grouping genetic algorithms Hybrid algorithms

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